{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d441f2b2-8a3f-41ad-8316-456e0a21ac9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import butter, filtfilt, find_peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd098dd5-abfe-4bee-850e-ac462c62e96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# global variables\n",
    "ppg_excel_file_path=\"Data File/PPG-BP dataset.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5e44632-4734-40fa-9573-89ae27908ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to get df for the patients data\n",
    "def get_df():\n",
    "    df = pd.read_excel(ppg_excel_file_path)\n",
    "    df.columns = df.iloc[0]  # Assign first row as column names\n",
    "    df = df[1:].reset_index(drop=True)  # Remove the first row and reset index\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dda7fbd7-6f75-4ec8-be5b-c3fc910ebaaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper function used to create a list of numbers from a txt file\n",
    "def load_ppg_signal(file_path):\n",
    "    with open(file_path, 'r') as f:\n",
    "        content=f.read()\n",
    "        numbers=[int(float(num)) for num in re.split(r'\\s+',content.strip())]\n",
    "    return numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76210c84-ae80-4331-acaa-7b23b07b86ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#used to load the signal data for all the patients for all 3 segments\n",
    "def load_ppg_signals():\n",
    "    ppg_signal_data=[]\n",
    "    for i in range(3):\n",
    "        ppg_signal_data_i=[]\n",
    "        for j in range(219):\n",
    "            subject_ID=df[\"subject_ID\"][j]\n",
    "            ppg_file_path=f\"Data File/0_subject/{subject_ID}_{i+1}.txt\"\n",
    "            ppg_signal=load_ppg_signal(ppg_file_path)\n",
    "            ppg_signal_data_i.append(ppg_signal[:2100])\n",
    "        ppg_signal_data.append(ppg_signal_data_i)\n",
    "    return np.array(ppg_signal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b0c0201-9922-43e6-b3d6-6f9a870a01a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_bandpass_filter(signal, lowcut=0.5, highcut=5.0, fs=1000, order=2):\n",
    "    # Normalize cutoff frequencies to Nyquist frequency\n",
    "    nyquist = fs / 2\n",
    "    low = lowcut / nyquist\n",
    "    high = highcut / nyquist\n",
    "    \n",
    "    # Design Butterworth bandpass filter\n",
    "    b, a = butter(order, [low, high], btype='band')\n",
    "    \n",
    "    # Apply filter (zero-phase filtering with filtfilt)\n",
    "    filtered_signal = filtfilt(b, a, signal)\n",
    "    \n",
    "    return filtered_signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "926974a6-8310-4c3a-aeb8-c95c1660f535",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since the peaks are not accurate as the signal is having noise \n",
    "# we will remove noise by using bandpass filters both low and high pass filter\n",
    "# human heart rate BPM lies between [40-180] bpm \n",
    "# so in seconds it will be BPS [0.67 -3]\n",
    "# so we will use a bandpass filter with frequency range of [0.5 -5]\n",
    "#why noise\n",
    "# Motion Artifacts:\n",
    "# Cause: Physical movement of the subject (e.g., finger twitching, walking) alters \n",
    "# the sensor’s contact with the skin or the blood flow dynamics.\n",
    "# Effects:Introduces low-frequency baseline wander or high-frequency spikes.\n",
    "\n",
    "# Ambient Light Interference:\n",
    "# Cause: External light sources (e.g., sunlight, fluorescent lights) penetrate the sensor, adding unwanted signals.\n",
    "# Effects:Introduces low-frequency baseline wander\n",
    "def remove_noise_from_ppg(ppg_signal_data_array, lowcut=0.5, highcut=4.0, fs=1000, order=4):\n",
    "    # Get array dimensions\n",
    "    num_segments, num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    # Initialize output array with same shape as input\n",
    "    filtered_ppg_array = np.zeros_like(ppg_signal_data_array)\n",
    "    # Apply filter to each segment of each patient\n",
    "    for segment_idx in range(num_segments):\n",
    "        for patient_idx in range(num_patients):\n",
    "            # Extract raw signal\n",
    "            raw_signal = ppg_signal_data_array[segment_idx, patient_idx, :]\n",
    "            # Apply bandpass filter\n",
    "            filtered_signal = apply_bandpass_filter(raw_signal)\n",
    "            # Store filtered signal\n",
    "            filtered_ppg_array[segment_idx, patient_idx, :] = filtered_signal\n",
    "    \n",
    "    return filtered_ppg_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c919637a-25df-4ca9-8769-97741e9621b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ppg_signal(ppg_signal_data_array, i, j,flag):\n",
    "    # Sampling rate and time axis\n",
    "    sampling_rate = 1000  # Hz\n",
    "    time = np.arange(0, 2.1, 1/sampling_rate)  # 0 to 2.1 seconds\n",
    "    # Extract the signal for the ith patient and jth segment\n",
    "    signal = ppg_signal_data_array[j, i, :]\n",
    "    # Ensure time and signal lengths match\n",
    "    if len(time) != len(signal):\n",
    "        time = time[:len(signal)]\n",
    "    # Create a single plot\n",
    "    ylabel=\"Raw ADC Value (12-bit)\"\n",
    "    if flag:\n",
    "        ylabel=\"Filtered ADC Value\"\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    plt.plot(time, signal, label=f'Segment {j}', color='blue')\n",
    "    plt.title(f'Patient {i} - Segment {j}')\n",
    "    plt.xlabel('Time (seconds)')\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    # Adjust layout and show\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa106574-2893-4a6d-b130-578e9f693e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot with peaks and give heart rate estimated how to calculate we will tell\n",
    "#peak must be local maxima \n",
    "#min distance between peaks be 300 and why??\n",
    "# max BPM is 200\n",
    "# max IBI=60/200 which is 0.3\n",
    "#so min distance is 0.3*1000=300\n",
    "def plot_ppg_signal_with_peaks(ppg_signal_data_array, i, j,flag):\n",
    "    # Check array bounds\n",
    "    num_segments, num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    if j >= num_segments or i >= num_patients:\n",
    "        raise ValueError(f\"Invalid indices: j={j} (max {num_segments-1}), i={i} (max {num_patients-1})\")\n",
    "    \n",
    "    # Sampling rate and time axis\n",
    "    sampling_rate = 1000  # Hz\n",
    "    time = np.arange(0, 2.1, 1/sampling_rate)  # 0 to 2.1 seconds, 2100 points\n",
    "    \n",
    "    # Extract the signal\n",
    "    signal = ppg_signal_data_array[j, i, :]\n",
    "    \n",
    "    # Ensure time and signal lengths match\n",
    "    if len(time) != len(signal):\n",
    "        time = time[:len(signal)]\n",
    "    \n",
    "    # Detect peaks (value > mean, min distance 0.3s = 300 samples at 1000 Hz)\n",
    "    peaks, _ = find_peaks(signal, height=np.mean(signal), distance=300)\n",
    "    \n",
    "    # Calculate heart rate\n",
    "    if len(peaks) >= 2:\n",
    "        peak_times = peaks / sampling_rate  # Convert to seconds\n",
    "        inter_beat_intervals = np.diff(peak_times)  # Time between consecutive peaks\n",
    "        average_interval = np.mean(inter_beat_intervals)  # Average IBI in seconds\n",
    "        heart_rate = 60 / average_interval  # BPM\n",
    "        hr_text = f\"{heart_rate:.2f} BPM\"\n",
    "    else:\n",
    "        hr_text = \"Insufficient peaks to calculate\"\n",
    "    \n",
    "    # Create a single plot\n",
    "    ylabel=\"Raw ADC Value (12-bit)\"\n",
    "    if flag:\n",
    "        ylabel=\"Filtered ADC Value\"\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    plt.plot(time, signal, label=f'Segment {j}', color='blue')  # Continuous signal\n",
    "    plt.scatter(time[peaks], signal[peaks], s=50, color='red', marker='x', label='Peaks')  # Mark peaks\n",
    "    plt.title(f'Patient {i} - Segment {j} - Heart Rate: {hr_text}')\n",
    "    plt.xlabel('Time (seconds)')\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    # Adjust layout and show\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # Print result in one line\n",
    "    print(f\"Patient {i}, Segment {j} - Heart Rate: {hr_text}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c22d47c5-9249-44ac-90b6-86a5f7dc5482",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ppg_signal_all_segments(ppg_signal_data_array, i,flag,img_path):\n",
    "    # Check array bounds\n",
    "    num_segments, num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    # Sampling rate and time axis\n",
    "    sampling_rate = 1000  # Hz\n",
    "    time = np.arange(0, 2.1, 1/sampling_rate)  # 0 to 2.1 seconds\n",
    "    \n",
    "    # Create a figure with 1 row and 3 columns\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(15, 4), sharey=True)\n",
    "    \n",
    "    # Plot each segment\n",
    "    for j in range(3):  # Segments 0, 1, 2\n",
    "        # Extract the signal for the ith patient and jth segment\n",
    "        signal = ppg_signal_data_array[j, i, :]\n",
    "        \n",
    "        # Ensure time and signal lengths match\n",
    "        if len(time) != len(signal):\n",
    "            time = time[:len(signal)]\n",
    "        \n",
    "        # Plot on the respective subplot\n",
    "        ax = axes[j]\n",
    "        ax.plot(time, signal, label=f'Segment {j}', color='blue')\n",
    "        ax.set_title(f'Segment {j}')\n",
    "        ax.set_xlabel('Time (seconds)')\n",
    "        ax.grid(True)\n",
    "        ax.legend()\n",
    "    \n",
    "    # Set y-label on the first subplot\n",
    "    if not flag:\n",
    "        axes[0].set_ylabel(f'Patient {i}\\nRaw ADC Value (12-bit)')\n",
    "    else :\n",
    "        axes[0].set_ylabel(f'Patient {i}\\nFiltered ADC Value')\n",
    "    # Adjust layout and add overall title\n",
    "    plt.suptitle(f'Patient {i} - Segments 0, 1, 2', y=1.05)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(img_path, dpi=300, bbox_inches=\"tight\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eac4ac3d-b20a-43ff-afba-8bdce53eac4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ppg_signal_with_peaks_all_segments(ppg_signal_data_array, i,flag,img_path):\n",
    "    # Check array bounds\n",
    "    num_segments, num_patients, num_samples = ppg_signal_data_array.shape    \n",
    "    # Sampling rate and time axis\n",
    "    sampling_rate = 1000  # Hz\n",
    "    time = np.arange(0, 2.1, 1/sampling_rate)  # 0 to 2.1 seconds, 2100 points\n",
    "    \n",
    "    # Create a figure with 1 row and 3 columns\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(15, 4), sharey=True)\n",
    "    \n",
    "    # Plot each segment\n",
    "    heart_rates = {}\n",
    "    for s_idx in range(3):  # Segments 0, 1, 2\n",
    "        # Extract the signal\n",
    "        signal = ppg_signal_data_array[s_idx, i, :]\n",
    "        \n",
    "        # Ensure time and signal lengths match\n",
    "        if len(time) != len(signal):\n",
    "            time = time[:len(signal)]\n",
    "        \n",
    "        # Detect peaks (value > mean, min distance 0.3s = 300 samples at 1000 Hz)\n",
    "        peaks, _ = find_peaks(signal, height=np.mean(signal), distance=300)\n",
    "        \n",
    "        # Calculate heart rate\n",
    "        if len(peaks) >= 2:\n",
    "            peak_times = peaks / sampling_rate  # Convert to seconds\n",
    "            inter_beat_intervals = np.diff(peak_times)\n",
    "            average_interval = np.mean(inter_beat_intervals)\n",
    "            heart_rate = 60 / average_interval  # BPM\n",
    "            heart_rates[s_idx] = heart_rate\n",
    "        else:\n",
    "            heart_rates[s_idx] = None\n",
    "        \n",
    "        # Plot signal with peaks\n",
    "        ax = axes[s_idx]\n",
    "        ax.plot(time, signal, label=f'Segment {s_idx}', color='blue')\n",
    "        ax.scatter(time[peaks], signal[peaks], s=50, color='red', marker='x', label='Peaks')\n",
    "        hr_text = f\"{heart_rates[s_idx]:.2f} BPM\" if heart_rates[s_idx] is not None else \"N/A\"\n",
    "        ax.set_title(f'Segment {s_idx} - HR: {hr_text}')\n",
    "        ax.set_xlabel('Time (seconds)')\n",
    "        ax.grid(True)\n",
    "        ax.legend()\n",
    "    \n",
    "    # Set y-label on the first subplot\n",
    "    if not flag:\n",
    "        axes[0].set_ylabel(f'Patient {i}\\nRaw ADC Value (12-bit)')\n",
    "    else:\n",
    "        axes[0].set_ylabel(f'Patient {i}\\nFiltered ADC Value')\n",
    "    # Adjust layout and add title\n",
    "    plt.suptitle(f'PPG Signals for Patient {i} - Segments 0, 1, 2', y=1.05)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(img_path, dpi=300, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    \n",
    "    # Print heart rates in one line\n",
    "    hr_output = f\"Patient {i} - \"\n",
    "    for s_idx in range(3):\n",
    "        hr = heart_rates[s_idx]\n",
    "        hr_text = f\"{hr:.2f} BPM\" if hr is not None else \"N/A\"\n",
    "        hr_output += f\"Segment {s_idx}: {hr_text}, \"\n",
    "    print(hr_output.rstrip(\", \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97f328ac-6455-4779-8479-8778d8472ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine all the signals\n",
    "def combine_all_segments(ppg_signal_data_array):\n",
    "    num_segments, num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    combined_samples = num_samples * num_segments  # 2100 * 3 = 6300\n",
    "    combined_signals = np.zeros((num_patients, combined_samples), dtype=ppg_signal_data_array.dtype)\n",
    "    for patient_idx in range(num_patients):\n",
    "        combined_signal = np.concatenate([\n",
    "            ppg_signal_data_array[0, patient_idx, :],  # Segment 0\n",
    "            ppg_signal_data_array[1, patient_idx, :],  # Segment 1\n",
    "            ppg_signal_data_array[2, patient_idx, :]   # Segment 2\n",
    "        ])\n",
    "        combined_signals[patient_idx, :] = combined_signal\n",
    "    \n",
    "    return combined_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2a58d7f-ae3d-45db-b195-bb3279c8689c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_noise_from_combined_ppg(ppg_signal_data_array, lowcut=0.5, highcut=4.0, fs=1000, order=4):\n",
    "    # Get array dimensions\n",
    "    num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    # Initialize output array with same shape as input\n",
    "    filtered_ppg_array = np.zeros_like(ppg_signal_data_array)\n",
    "    # Apply filter to each segment of each patient\n",
    "    for patient_idx in range(num_patients):\n",
    "        # Extract raw signal\n",
    "        raw_signal = ppg_signal_data_array[patient_idx, :]\n",
    "        # Apply bandpass filter\n",
    "        filtered_signal = apply_bandpass_filter(raw_signal)\n",
    "        # Store filtered signal\n",
    "        filtered_ppg_array[patient_idx, :] = filtered_signal\n",
    "    return filtered_ppg_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "23c49251-034f-4d50-8c78-ebebbacbbf98",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_combined_ppg_signal(ppg_signal_data_array, i,flag):\n",
    "    # Sampling rate and time axis\n",
    "    sampling_rate = 1000  # Hz\n",
    "    time = np.arange(0, 6.3, 1/sampling_rate)  # 0 to 6.3 seconds\n",
    "    # Extract the signal for the ith patient\n",
    "    signal = ppg_signal_data_array[i, :]\n",
    "    # Ensure time and signal lengths match\n",
    "    if len(time) != len(signal):\n",
    "        time = time[:len(signal)]\n",
    "    # Create a single plot\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    plt.plot(time, signal, color='blue',label=f'combined all segments')\n",
    "    plt.title(f'Patient {i}')\n",
    "    plt.xlabel('Time (seconds)')\n",
    "    plt.ylabel('Raw ADC Value (12-bit)')\n",
    "    if not flag:\n",
    "        plt.ylabel('Raw ADC Value (12-bit)')\n",
    "    else:\n",
    "        plt.ylabel('Filtered ADC Value')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    # Adjust layout and show\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30a54ffa-21a0-410f-8c37-7fb641b4c4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_combined_ppg_signal_with_peaks(ppg_signal_data_array, i,flag):\n",
    "    # Check array bounds\n",
    "    num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    # Sampling rate and time axis\n",
    "    sampling_rate = 1000  # Hz\n",
    "    time = np.arange(0, 6.3, 1/sampling_rate)  # 0 to 2.1 seconds, 2100 points\n",
    "    # Extract the signal\n",
    "    signal = ppg_signal_data_array[i, :]\n",
    "    # Ensure time and signal lengths match\n",
    "    if len(time) != len(signal):\n",
    "        time = time[:len(signal)]\n",
    "    # Detect peaks (value > mean, min distance 0.3s = 300 samples at 1000 Hz)\n",
    "    peaks, _ = find_peaks(signal, height=np.mean(signal), distance=300)\n",
    "    # Calculate heart rate\n",
    "    if len(peaks) >= 2:\n",
    "        peak_times = peaks / sampling_rate  # Convert to seconds\n",
    "        inter_beat_intervals = np.diff(peak_times)  # Time between consecutive peaks\n",
    "        average_interval = np.mean(inter_beat_intervals)  # Average IBI in seconds\n",
    "        heart_rate = 60 / average_interval  # BPM\n",
    "        hr_text = f\"{heart_rate:.2f} BPM\"\n",
    "    else:\n",
    "        hr_text = \"Insufficient peaks to calculate\"\n",
    "    \n",
    "    # Create a single plot\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    plt.plot(time, signal, label=\"combined all segments\", color='blue')  # Continuous signal\n",
    "    plt.scatter(time[peaks], signal[peaks], s=50, color='red', marker='x', label='Peaks')  # Mark peaks\n",
    "    plt.title(f'Patient {i} - Heart Rate: {hr_text}')\n",
    "    plt.xlabel('Time (seconds)')\n",
    "    if not flag:\n",
    "        plt.ylabel('Raw ADC Value (12-bit)')\n",
    "    else:\n",
    "        plt.ylabel('Filtered ADC Value')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    \n",
    "    # Adjust layout and show\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # Print result in one line\n",
    "    print(f\"Patient {i}- Heart Rate: {hr_text}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1dfa0f5-d824-4efb-af2e-0bf740d69655",
   "metadata": {},
   "outputs": [],
   "source": [
    "# what we require now is df,combined without noise,estimated heartbeat rate,which we will add to the df\n",
    "#then we will clean the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f921c59f-520a-4337-9573-598e1bd552d6",
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num.</th>\n",
       "      <th>subject_ID</th>\n",
       "      <th>Sex(M/F)</th>\n",
       "      <th>Age(year)</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Systolic Blood Pressure(mmHg)</th>\n",
       "      <th>Diastolic Blood Pressure(mmHg)</th>\n",
       "      <th>Heart Rate(b/m)</th>\n",
       "      <th>BMI(kg/m^2)</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>cerebral infarction</th>\n",
       "      <th>cerebrovascular disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>152</td>\n",
       "      <td>63</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>97</td>\n",
       "      <td>27.268006</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>157</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>20.284799</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>150</td>\n",
       "      <td>47</td>\n",
       "      <td>101</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>20.888889</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>172</td>\n",
       "      <td>65</td>\n",
       "      <td>136</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>21.971336</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>155</td>\n",
       "      <td>65</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>27.055151</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 Num. subject_ID Sex(M/F) Age(year) Height(cm) Weight(kg)  \\\n",
       "0    1          2   Female        45        152         63   \n",
       "1    2          3   Female        50        157         50   \n",
       "2    3          6   Female        47        150         47   \n",
       "3    4          8     Male        45        172         65   \n",
       "4    5          9   Female        46        155         65   \n",
       "\n",
       "0 Systolic Blood Pressure(mmHg) Diastolic Blood Pressure(mmHg)  \\\n",
       "0                           161                             89   \n",
       "1                           160                             93   \n",
       "2                           101                             71   \n",
       "3                           136                             93   \n",
       "4                           123                             73   \n",
       "\n",
       "0 Heart Rate(b/m) BMI(kg/m^2)          Hypertension Diabetes  \\\n",
       "0              97   27.268006  Stage 2 hypertension      NaN   \n",
       "1              76   20.284799  Stage 2 hypertension      NaN   \n",
       "2              79   20.888889                Normal      NaN   \n",
       "3              87   21.971336       Prehypertension      NaN   \n",
       "4              73   27.055151       Prehypertension      NaN   \n",
       "\n",
       "0 cerebral infarction cerebrovascular disease  \n",
       "0                 NaN                     NaN  \n",
       "1                 NaN                     NaN  \n",
       "2                 NaN                     NaN  \n",
       "3                 NaN                     NaN  \n",
       "4                 NaN                     NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=get_df()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c71070fc-59b1-4942-8776-6443b3daffb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppg_data_with_noise_uncombined=load_ppg_signals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2234f55-5752-452e-bdf0-4972c28a5823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 219, 2100)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppg_data_with_noise_uncombined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6561d774-0281-435c-856c-7690fd64dc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppg_data_with_noise=combine_all_segments(ppg_data_with_noise_uncombined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ba4f573c-59f5-4f36-9f48-13b3685a3088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219, 6300)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppg_data_with_noise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d3f09ec-cc94-41a7-8e83-e215d1a9d301",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppg_data=remove_noise_from_combined_ppg(ppg_data_with_noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a5e6693-0379-40f0-b169-2d67610e4aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219, 6300)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppg_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2cbea681-3b77-4cbb-a6ba-9b4afaa59d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_combined_ppg_signal(ppg_data_with_noise,0,False)\n",
    "plot_combined_ppg_signal(ppg_data,0,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3519d8b-f1b8-4ee9-9ef5-b4008361c843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient 0- Heart Rate: 101.37 BPM\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient 0- Heart Rate: 100.69 BPM\n"
     ]
    }
   ],
   "source": [
    "plot_combined_ppg_signal_with_peaks(ppg_data_with_noise,0,False)\n",
    "plot_combined_ppg_signal_with_peaks(ppg_data,0,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "55a1dc56-60e0-4c7e-9228-efd555817dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_heart_rate(ppg_signal_data_array, patient_idx, sampling_rate=1000):\n",
    "    # Check array bounds\n",
    "    num_patients, num_samples = ppg_signal_data_array.shape\n",
    "    # Extract the signal for the specified patient\n",
    "    signal = ppg_signal_data_array[patient_idx, :]\n",
    "    # Detect peaks (value > mean, min distance 0.3s = 300 samples at 1000 Hz)\n",
    "    peaks, _ = find_peaks(signal, height=np.mean(signal), distance=300)\n",
    "\n",
    "    # Calculate heart rate\n",
    "    if len(peaks) >= 2:\n",
    "        peak_times = peaks / sampling_rate  # Convert sample indices to seconds\n",
    "        inter_beat_intervals = np.diff(peak_times)  # Time between consecutive peaks in seconds\n",
    "        average_interval = np.mean(inter_beat_intervals)  # Average IBI in seconds\n",
    "        heart_rate = 60 / average_interval  # Convert to beats per minute (BPM)\n",
    "        return heart_rate\n",
    "    else:\n",
    "        return \"Insufficient peaks to calculate heart rate\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4bedfb76-be98-4aa2-ba3c-4add220bd8a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calulate_heart_rates(ppg_signal_data_array):\n",
    "    num_patients,num_samples=ppg_signal_data_array.shape\n",
    "    heart_rate=np.zeros(num_patients)\n",
    "    for i in range(num_patients):\n",
    "        heart_rate[i]=calculate_heart_rate(ppg_signal_data_array,i)\n",
    "    return heart_rate\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "7232ebc1-ae51-4dc9-86b8-b31d177b6ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100.68991236,  84.8956491 ,  86.47313156,  94.70361277,\n",
       "        88.59357697,  84.53680874,  93.37713989,  88.138083  ,\n",
       "        57.60368664,  79.00677201,  93.75      ,  92.69988413,\n",
       "        84.01680336,  79.57559682,  88.47926267,  84.95575221,\n",
       "        97.64918626,  86.90928843,  85.7449089 ,  85.65310493,\n",
       "        87.62779053, 107.52688172,  87.99266728,  84.92569002,\n",
       "        75.20143241,  66.15214994,  84.46243181,  92.65614276,\n",
       "        94.970102  ,  72.91666667,  71.77033493,  87.17762441,\n",
       "        73.90679532,  77.70582794,  63.60424028,  83.58208955,\n",
       "        98.70224822,  72.90400972,  91.75870858,  74.62686567,\n",
       "        72.80291212, 114.52368558, 105.63380282,  83.20332813,\n",
       "        88.85597927,  58.45674201,  92.44992296,  73.60672976,\n",
       "        94.77009477,  76.21121394,  88.23529412,  70.02528691,\n",
       "       106.7425725 ,  74.12636781,  80.12209081,  85.45486915,\n",
       "        94.09304757,  77.90762382,  70.83825266,  90.49773756,\n",
       "        77.31958763,  69.16426513,  86.047941  ,  56.74295442,\n",
       "        85.06113769,  72.2518493 ,  74.51873318,  82.30452675,\n",
       "        80.66064913,  71.18845165,  67.13912719,  67.8093803 ,\n",
       "        70.51909892,  74.28807264,  70.82431635,  88.62629247,\n",
       "        64.99368117,  89.93816751,  76.89491029,  69.23076923,\n",
       "        77.64836384,  60.92607636,  78.53403141,  66.00660066,\n",
       "        72.1298337 ,  69.11115377,  81.77570093,  70.65948856,\n",
       "        84.94071846,  86.90928843,  80.57747188,  80.6296794 ,\n",
       "        85.88298443,  94.1586748 ,  82.37515016,  59.0086546 ,\n",
       "        77.39082366,  66.6173205 ,  69.4980695 ,  91.60305344,\n",
       "        78.2414307 ,  70.86614173,  73.00750355,  89.98875141,\n",
       "        87.11082433,  87.00380642,  85.09129587,  76.78244973,\n",
       "        64.1025641 ,  69.86221618,  75.70295602,  66.7903525 ,\n",
       "        72.01646091,  79.60576194,  63.49206349,  78.07417046,\n",
       "        88.0572372 ,  92.14820503,  73.1261426 ,  89.68609865,\n",
       "        74.56062489,  72.33273056,  91.41115978,  66.80274634,\n",
       "        69.17755573,  65.98240469,  68.59756098,  88.47926267,\n",
       "        78.62223886,  77.3480663 ,  72.37635706,  89.85879332,\n",
       "       103.54745925,  85.27918782,  74.08931879,  60.35003018,\n",
       "        89.36883262,  60.89309878,  84.25275827,  79.93909402,\n",
       "        92.48554913,  69.08462867,  69.19085143,  76.76841528,\n",
       "        80.42895442,  85.65310493,  77.10666422,  87.68724881,\n",
       "        88.77198751,  75.582616  ,  63.95452123,  55.98059339,\n",
       "        58.66249511,  59.94005994,  77.82101167,  67.82215524,\n",
       "        75.45587927,  60.50826946,  67.40310803,  72.01440288,\n",
       "        66.72845227,  60.86427267,  65.30256857,  57.00171005,\n",
       "        90.7105661 ,  76.57945118,  66.40841173,  89.56709239,\n",
       "        81.54943935, 109.36930368,  72.23942208,  77.5862069 ,\n",
       "        77.78738116,  62.89308176,  84.38818565,  73.755378  ,\n",
       "       100.55865922,  94.1586748 ,  91.80550833,  73.93715342,\n",
       "        68.79419071,  77.97994801,  77.33382434,  89.91008991,\n",
       "        69.83511154,  63.62672322,  78.49000187,  87.27272727,\n",
       "       105.95090941,  83.33333333,  73.96970764,  77.61966365,\n",
       "        58.93909627,  83.97480756,  76.74036178,  65.84964331,\n",
       "        79.83273142,  76.05939877,  87.71929825,  90.48257373,\n",
       "        85.731782  ,  93.2642487 ,  65.63354603,  87.27272727,\n",
       "        78.38745801,  92.18163196,  77.77777778,  88.9218229 ,\n",
       "        96.35974304,  86.20689655,  85.10638298,  72.68322229,\n",
       "        68.61063465, 105.48523207,  93.65244537,  87.08272859,\n",
       "        89.33556672,  66.72845227,  68.54531607])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calulate_heart_rates(ppg_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "205297d2-1505-41f2-9160-6f7636965d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Num.', 'subject_ID', 'Sex(M/F)', 'Age(year)', 'Height(cm)',\n",
       "       'Weight(kg)', 'Systolic Blood Pressure(mmHg)',\n",
       "       'Diastolic Blood Pressure(mmHg)', 'Heart Rate(b/m)', 'BMI(kg/m^2)',\n",
       "       'Hypertension', 'Diabetes', 'cerebral infarction',\n",
       "       'cerebrovascular disease'],\n",
       "      dtype='object', name=0)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1ba48b4b-6c9d-4494-bcc6-00d75999a87a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 219 entries, 0 to 218\n",
      "Data columns (total 14 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Num.                            219 non-null    object\n",
      " 1   subject_ID                      219 non-null    object\n",
      " 2   Sex(M/F)                        219 non-null    object\n",
      " 3   Age(year)                       219 non-null    object\n",
      " 4   Height(cm)                      219 non-null    object\n",
      " 5   Weight(kg)                      219 non-null    object\n",
      " 6   Systolic Blood Pressure(mmHg)   219 non-null    object\n",
      " 7   Diastolic Blood Pressure(mmHg)  219 non-null    object\n",
      " 8   Heart Rate(b/m)                 219 non-null    object\n",
      " 9   BMI(kg/m^2)                     219 non-null    object\n",
      " 10  Hypertension                    219 non-null    object\n",
      " 11  Diabetes                        38 non-null     object\n",
      " 12  cerebral infarction             20 non-null     object\n",
      " 13  cerebrovascular disease         25 non-null     object\n",
      "dtypes: object(14)\n",
      "memory usage: 24.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "ddc1dfda-3e0d-49a7-b8ec-35824dfb7392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num.</th>\n",
       "      <th>subject_ID</th>\n",
       "      <th>Sex(M/F)</th>\n",
       "      <th>Age(year)</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Systolic Blood Pressure(mmHg)</th>\n",
       "      <th>Diastolic Blood Pressure(mmHg)</th>\n",
       "      <th>Heart Rate(b/m)</th>\n",
       "      <th>BMI(kg/m^2)</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>cerebral infarction</th>\n",
       "      <th>cerebrovascular disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>152</td>\n",
       "      <td>63</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>97</td>\n",
       "      <td>27.268006</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>157</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>20.284799</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>150</td>\n",
       "      <td>47</td>\n",
       "      <td>101</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>20.888889</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>172</td>\n",
       "      <td>65</td>\n",
       "      <td>136</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>21.971336</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>155</td>\n",
       "      <td>65</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>27.055151</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 Num. subject_ID Sex(M/F) Age(year) Height(cm) Weight(kg)  \\\n",
       "0    1          2   Female        45        152         63   \n",
       "1    2          3   Female        50        157         50   \n",
       "2    3          6   Female        47        150         47   \n",
       "3    4          8     Male        45        172         65   \n",
       "4    5          9   Female        46        155         65   \n",
       "\n",
       "0 Systolic Blood Pressure(mmHg) Diastolic Blood Pressure(mmHg)  \\\n",
       "0                           161                             89   \n",
       "1                           160                             93   \n",
       "2                           101                             71   \n",
       "3                           136                             93   \n",
       "4                           123                             73   \n",
       "\n",
       "0 Heart Rate(b/m) BMI(kg/m^2)          Hypertension Diabetes  \\\n",
       "0              97   27.268006  Stage 2 hypertension      NaN   \n",
       "1              76   20.284799  Stage 2 hypertension      NaN   \n",
       "2              79   20.888889                Normal      NaN   \n",
       "3              87   21.971336       Prehypertension      NaN   \n",
       "4              73   27.055151       Prehypertension      NaN   \n",
       "\n",
       "0 cerebral infarction cerebrovascular disease  \n",
       "0                 NaN                     NaN  \n",
       "1                 NaN                     NaN  \n",
       "2                 NaN                     NaN  \n",
       "3                 NaN                     NaN  \n",
       "4                 NaN                     NaN  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2129ad6c-65db-4699-a6d5-e429a0f14d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "def normalize_signal_per_array(signal_array):\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    normalized_arrays = [scaler.fit_transform(sub_array.reshape(-1, 1)).flatten() for sub_array in signal_array]\n",
    "    return np.array(normalized_arrays, dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "6d3b015c-1a18-45a9-b7c0-b23dd16ab13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_ppg_data=normalize_signal_per_array(ppg_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "993181d3-0368-4b23-907b-0433bddb984a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ARbfx9ttvY/LkyXjvvfdw5MgRzJ8/H1FRUQCAjIwM3HvvvYiIiMDOnTuxePFirFmzBiNGjChUxrp163DhwgVs2rQJU6dOxdixY3HfffchIiICf/31F15++WW8/PLLOHv2bKHfvfXWW3jzzTexd+9edO3aFffffz+ulGJqmZGRgU8//RRz587Fpk2bcObMGYwePbrg85kzZ+Kdd97BRx99hKNHj2LixIl477338NNPPwEA0tPTcd9996Fx48bYvXs3xo4dW+j3JZGTk4Px48dj//79+O2335CQkIBhw4Y5e4lvY/r06Vi+fDl++eUXHD9+HPPmzUOdOnUAAJIkoX///khOTobdbsfu3bvRtm1b9OrVC1dvTf5+/vlnfPTRR5g8eTJ2796NWrVq4ZtidmRcbZeMjAz07NkT5cuXx6ZNm7Bly5aCxY2cnJyCctevX4+TJ09i/fr1+OmnnzB79mzMnj0bAPDrr7+iRo0a+PDDD5GUlFSwKPLKK68gOzsbmzZtwsGDBzF58mSUL1/e7WtYJhJRiOvXr0sApOvXr4sWpURycnKk3377TcrJyREtimY89pgksSFakj79VM7Pny9aMuOhZ3+IjGTt0KABe33ihNw2f/yhefVEMVy8KEnvvitJI0dK0muv5UrDh+/16rGBcB5feFYQzkF9wTfJzMyUjhw5ImVmZkqSJEk3b8rPbL3/bt50Tua0tDQpMDBQmjlzZrGfz5gxQ4qIiJBuKgpcuXKl5OfnJyUnJ0uSJElDhw6VateuLeXl5RV8p3HjxlL37t0LXufm5krlypWTFixYIEmSJCUkJEgApI8//rjgOw6HQ6pRo4Y0efJkSZIkaf369RIA6dq1a5IkSdKsWbMkANKJEycKfvPVV19JUVFRBa9r1qwpzS8yeR0/frzUpUsXSZIk6bvvvpMiIyOl9PT0gs+/+eYbCYC0d+/esi/YLXbs2CEBkG7cuFGirOHh4bf9Li8vT7p27Zo0YsQI6Z577pHy8/Nv+87atWulsLAwKSsrq9D79evXl7777jtJkiSpU6dO0iuvvFLo827dukmtWrUqeO1Ou/zwww9S48aNC8mVnZ0tBQcHS6tXry5Ubm5ubsF3HnnkEWnw4MEFr2vXri19/vnnheRr2bKlNHbs2Nv+3+Ioei8pcVZ3pJ1uwpCsWsXSYcOAN98E6tVjr+fMESaSz3Ptmryb/f77LK1fn523B4CVK8XI5etMnw5MmMCsEL74woqvv26Nv/8WLRVBEARBuM7Ro0eRnZ2NXr16lfh5q1atUK5cuYL3unXrhvz8fBw/frzgvebNm8PPT1ZzoqKi0LJly4LXVqsVFStWvM0ku0uXLgV5f39/tG/fHkePHi1R3pCQENSvX7/gddWqVQvKvHTpEs6ePYvnnnsO5cuXL/ibMGECTp48Wej/CQkJKVaGkti7dy8eeOAB1K5dG6GhoejRowcA4MyZM2X+tjiGDh2Kffv2oXHjxnjttdcQFxdX8Nnu3btx8+ZNVKxYsdD/kZCQUPB/HD9+HB07dixUZtHXgOvtsnv3bpw4cQKhoaEF9UZGRiIrK6ugbl6u1WoteK1sh5J47bXXMGHCBHTr1g0ffPABDhw44Mylcht/TUsnCDc4exa4fp3lX3qJpUOGMMXijz+YR3ObTZx8vsrGjXL+4YflfOfOwJkzwI8/Mq/mhH44HOy+AIB77gEOHpRw6ZIFEyda8fPPYmUjfIusLODxx4FTp9jrBg2ABQuAgACxchEEIRMSAty8yc5Mp6WlISwsrJACpHXdzhAcHFzq55IklejITPm+rchE0WKxFPtefn5+mTKV5jituDKlW7b0vOyZM2eiU6dOhb7HFUT+XVdIT09HTEwMYmJiMG/ePFSuXBlnzpxB3759C5lcu0Lbtm2RkJCAVatWYc2aNXj00UfRu3dvLFmyBPn5+ahatWqh8+0cZRiyotepuP/N1XbJz89Hu3bt8HMxk5rKlSuXWm5Zbfv888+jb9++WLlyJeLi4jBp0iR89tlnePXVV0v9nbvQTjdhOJYtk/Pc/8Ljj8vv7dqlqzjELbhfD4sFUD4TY2JYmp2tv0y+zu7dcv6NN4DatdkDbv168qxK6MumTcBvvwEHDrC/X39lESgIgjAOFguLCCPiz1mH3w0bNkRwcDDWrl1b7OfNmjXDvn37kM497QL4888/4efnh0aNGnl8jbYrBq7c3Fzs3r0bTZo0causqKgoVK9eHadOnUKDBg0K/dWtWxcA+3/279+PzMzMYmUojmPHjuHy5cv4+OOP0b17dzRp0sQjJ2qcsLAwDB48GDNnzsSiRYuwdOlSXL16FW3btkVycjL8/f1v+z8qVaoEAGjcuDF27NhRqLxdKkzY27Zti3/++QdVqlS5rW5XQrcFBAQgr5j4wzVr1sTLL7+MX3/9FW+++SZmzpzpscwlQUo3YTg+/ZSlDz4IBAWxfLNmAL+3yMRcDL/8wtKiC4APPsjS3FygFAssQgPmzmWpzQb07w989RV7oCQlWXDwoEDBCJ8iPR3o25fl77gDaN6c5e++2zXnSQRBEEFBQfjXv/6FMWPGYM6cOTh58iS2b9+OH374AQDwxBNPICgoCEOHDsWhQ4ewfv16vPrqq3jqqacKnK15wldffYVly5bh2LFjeOWVV3Dt2jU8++yzbpc3duxYTJo0Cf/973/x999/4+DBg5g1axamTp0KABgyZAj8/Pzw3HPP4ciRI7Db7fiUT4RLoFatWggICMD06dNx6tQpLF++HOPHj3dbRgCYNm0aFi5ciGPHjuHvv//G4sWLER0djQoVKqB3797o0qULBg4ciNWrVyMxMRFbt27Fu+++W6BYv/rqq/jhhx/w008/4Z9//sGECRNw4MABj8OrPfHEE6hUqRIeeOABbN68GQkJCdi4cSNef/11nDt3zuly6tSpg02bNuH8+fO4fMsr88iRI7F69WokJCRgz549WLduHZo2beqRvKVBSjdhOC5dYmnbtoXf5wuYhw7pKw/BSE5madFnWkSEnKd43frCF8NbtWK7CC1ayJ9pGPWCIAqhtLgYNqywZdLFi7qLQxCEyXnvvffw5ptv4v3330fTpk0xePDggp3ckJAQrF69GlevXkWHDh3w8MMPo1evXvjyyy9Vqfvjjz/G5MmT0apVK2zevBn/+9//CnZz3eH555/H999/j9mzZ6Nly5a4++67MXv27IKd7vLly2PFihU4cuQI2rRpg3feeQeTJ08utczKlStj9uzZWLx4MZo1a4aPP/64TEW9LMqVK4fJkyejffv26NChAxITE2G32+Hn5weLxQK73Y677roLzz77LBo1aoTHHnsMiYmJBQsdTzzxBN5++22MHj26wFR92LBhCOK7Z24SEhKCTZs2oVatWhg0aBCaNm2KZ599FpmZmQgLC3O6nA8//BCJiYmoX79+gVl6Xl4eXnnlFTRt2hT33nsvGjdufFs4NzWxSO4cJvBi0tLSEB4ejuvXr7vUmHricDhgt9sRGxt72xkGs5OZKZ/7OXIEUC44/fgj8NxzLJ+f77ypkrejR3+4cgXgz5w//wS6di38+SOPAEuWAO3bAzt3aiICUYTUVHnBY/ZsYOhQ1hf69k3C+vW10LgxcOyYSAkJ0ej1rOjXj/nbaNJEtnbx9wfy8oAnn5QtMghxePO8gSiZrKwsJCQkoG7duoWUHxFnugljomVf6NOnD6KjozHXCx4CJd1LgPO6I91phKE4ckTOFz1Cw80XAdo90ZvDh+V8Mc4oC6wQ6Ly9fihN+e+/X87XqnUDAKBw4uoTpKcD770HjBwJKByaEhojSUzhBoA2beT3ufPdefPIxJwgCMLbycjIwNSpU3H48GEcO3YMH3zwAdasWYOhQ4eKFs0wkNJNGAoedqo4pxvVq8v55cv1k4mQnahVr852sIoSGyvnaYKtD3v2sDQ6urCJf5s28oqULzm3W7aMeXL/73+Bjz8WLY3vsH+/nB8xQs5/9JGcVy7aEQRBEN4HN0Hv3r072rVrhxUrVmDp0qXo3bu3aNEMAyndhKHg8blLClHYrBlLFeEDCR1Yt46lt44g3Yby/D2ZNOvD/PksrVOn8Pt8pxuQFXNvZ+tW4Kmn5Nfffw989504eXyJJUvkvHLc7t5dzv/6q37yEARBEPoTHByMNWvW4OrVq0hPT8eePXswaNAg0WIZClK6CUPB49rzUGFFadWKpW6GISTchO9eF1XwOMoQYps2aS4OAeDGLd26ZcvC7yuPZPlKyKYZM25/77339JfDF0lIYGnjxoWtkywWoH59lidzf4IgCMLXIaWbMBR//slS5fltJQMGsHTFCuakh9CHFStYqjw7XJSHHmLpb79pLo7Pk5+PgpBgxUUyefrpfACFdyG9lZQU4KefWP7ZZ4GlS1n+0iXa7dYaSZItLkaOvP3z119nKYV5JAiCIHwdUroJw5CZKeeLOlHjKE0Wb4XZIzRGkoDAQJavV6/k73GHjVeuaC+Tr6Ps+8owYZwKFZhpwtWrOgkkEH70AQDeegu47z759X//q788vsTNm3K+W7fbP1dGOVCO7wRB6AsFKiIIz8jPz/e4jGJcIhGEGJQOeZRO05TUqAGEhjLT2h075J1vQjvS02WHXI0bl/y9Xr2AWbPIg7ke8LBs5cuzv6LcfbeEL77wjfP1U6awtFs3ebFu7lx2xvvoUbYIVLGiOPm8GW7VUjRGPEfpzXz5cmDwYF3EIgjiFjabDRaLBZcuXULlypVhuXUGJD8/Hzk5OcjKyqKQYT4O9YXSkSQJOTk5uHTpEvz8/BAQEOB2WaR0E4Zh9245X1oMboeDpXv3ktKtB0lJLA0IYF7lS4Kfw6cFde3h9wq/F4rSurXcCHl5sq8EbyM3V/asf9dd8vvKYxDbtwP9++srl6+wcSNLy5cvfsz282NWMtnZzLs8Kd0EoS9WqxU1atTAuXPnkJiYWPC+JEnIzMxEcHBwgSJO+CbUF5wjJCQEtWrV8mhhgpRuwjBwZzudO5f+ve7dgfh44PRp7WUiZAVPkkpfDFF6Nj9+vPRdccIzzpxhqVLRVFK1qpzfuxdo3157mUTAlT4AUIYCDQsDoqKAixeBNWtI6daKlBSWDhxY8ncGD2Znun3B6oIgjEj58uXRsGFDOBSrtA6HA5s2bcJdd90Fm80mUDpCNNQXysZqtcLf39/jRQlSugnDkJ7O0pI8l3M6dGBKN8Xq1ocNG1jaoEHp3wsJkfPbt5PSrSX/+x9LS1Km/f2ZZUJODgun5a1KN3egBtze3/r2Zcret98Cn3+ur1y+AnewWFKIRwB45BHWDsrjQwRB6IvVaoVVYfJktVqRm5uLoKAgUrR8HOoL+kHG+4RhuHiRpUVDIBWFK380NuhDbi5LlbunJREZyVK+A0ZoQ3g4S3lIpuLg7XXtmvbyiIL3sx49bv+Mv5eVpZc0vgefw5fk+BIovOCTmqqpOARBeBk5Ocxy7vhx4Pp10dIQhGeYRunOzc3Fu+++i7p16yI4OBj16tXDhx9+WMibnCRJGDt2LKpVq4bg4GD06NEDhw8fFig14Qp8964sp0d8VyUpSXbwRWgH90b+8MNlf3fIEJb6gtdskfA2Kc5jNOexx1i6YIH28ohi9WqWvvrq7Z8pz3XTY0B9Ll2Swza2alXy96Kj5fyBA9rKRBCEd9G1K1vUa9KEOdjlmzMEYUZMo3RPnjwZ3377Lb788kscPXoUU6ZMwSeffILp06cXfGfKlCmYOnUqvvzyS+zcuRPR0dHo06cPbty4IVBywlXq1Cn984YN5Tyd69ae7dtZWqlS2d+tUoWl+/ZpJo7Pk5sr7xiWtkAVGspSbw2tp4zeoRwTONzqAmDHUQh1US5kREQ495u//9ZGFoIgvI+//pJ9yvj5sSOIH38sViaC8ATTKN3btm3DAw88gP79+6NOnTp4+OGHERMTg1234hNJkoRp06bhnXfewaBBg9CiRQv89NNPyMjIwPz58wVLT5SFUnlu1qz071qtsunsn39qJxPBnKclJ7O8MxNrruhxj9KE+hw8KOdLaxMe0/7KFe/0KK9UpIvzN2CxyA6+fvlFF5F8is2bWdqqVekOFgE5ygT/DUEQRGk4HPIzzGqVjwtNm1Y40g1BmAnTOFK788478e233+Lvv/9Go0aNsH//fmzZsgXTpk0DACQkJCA5ORkxMTEFvwkMDMTdd9+NrVu34qWXXiq23OzsbGQrbJTT0tIAMG9+jpLi8QiGy2VU+dzh1CkLeHcMCnKUGAqJY7X6A7Bg5848PPmk5wHrzYyW/YE5t2OH51u1KrtdGjdm7Xjxonf1TyNx5ox8r0hS4TZR9gVm8svaLiXFUWjn1xuIi/MDwA4V+/sX3zdbtPDDb79ZsW2bb/ZHLceGHTusAPwQGJgPhyOv1O+Gh7Pv/vWXBIcjV3VZiLLxxnmDqyxfbsGCBX6QJOYT5rXX8tGhgxeuSDqB0fuD3W6Bw8Gec19/nYvGjYF169jrkSPzsW5d6WMO4TxG7wtmwNlrZxql+1//+heuX7+OJk2awGq1Ii8vDx999BEef/xxAEDyre24qKioQr+LiorC6VJskCdNmoRx48bd9n5cXBxClO6YDUi8F9lMbt8eDaATGje+Cru97O2QmjU74Ny5ajhwIBl2+y7tBTQBWvSHS5eCAcTA3z8Pf/5pL3NHKzU1AEA/AMCCBWsQHp6juky+zvr1NQG0RevWKbDbtxX7HbkvPAAA+OabnWjT5pI+AurE3r1tANRCu3bJsNv/KvY75cpFAmDbJStXlt1/vRUtxobTp7sCqIxKlRJhtx8s9btBQXUB3IHU1AzY7WtUl4VwHm+aN7jK8OG9kZJSruD1yZMpePfd4scOX8Go/WH8+E4AmEOIqKiVSE0F7rmnDdatq4UtW/zwyy+rUL48LeCpiVH7ghnIyMhw6numUboXLVqEefPmYf78+WjevDn27duHkSNHolq1ahiqCNBaNIaaJEmlxlV7++23MWrUqILXaWlpqFmzJmJiYhAWFqb+P6ICDocD8fHx6NOnj9e4909OZm1Uv34FxMbGlvn9Eyf8sG0bsHVrNae+781o2R9+/521S+XKfujf37nrPGwYS6tU6YNevXxzF0FL5s9nu7uNG1e6re8X7QvVq0s4f96Chg07IjbWu9riiSfY42vgwMoljgE9egBvv83ybdvGOuWB35vQcmwYOJCV9+STtRAbW7PU71atCnz/PXDxYjnce28s/ExzsM178MZ5gyv88osFKSlszHjssXwsXOiHXbuikZvbH/ff711jozMYuT9Ikjy+fPhhXsH43rEjUK0a+87Vq/fi0Ud928pRLYzcF8wCt5IuC9Mo3W+99Rb+/e9/47FbLnlbtmyJ06dPY9KkSRg6dCiib7lITU5ORlXFzColJeW23W8lgYGBCAwMvO19m81m+M5nBhmdhYc1qlzZDzZb2TMyHqKmUiWL11wDT9GiP/CQTElJrl/n5GR/CuumAVxhyckp+V7hfaFTJ+DXX4HUVO9rC/7/NGhghc1mLfY7PLQaAPzzjw21aukgmAFRe2zIy2PnuCUJaNmy7L7VvLmcv3jRd9tBFFevAidPAomJobBavWfe4Aqffy7nP/rIDwsXsvz06f546CExMhkBI84jz52T808+KY/vVauy8915ecCGDVa8+mrx4z7hHkbsC2bB2etmmvXmjIwM+BVZHrdarQUhw+rWrYvo6OhC5hE5OTnYuHEjunbtqqushOssWsTSssKFcdq0YenVq4W9GBPqwkNTKYxJyoSHquK/JdSFX1fuJKw0+P20eLFm4gghPV324N6xY+nf7dSJpd99p6lIPsW1a7JzvsaNy/5+uXLyAsiOHdrJRdzO1atA7dpAx442jBx5D4YP9z1FZf9+2fnWzz8D9eoBX37JXm/cSF71jcann7I0MBCoW7fwZzxg0a+/Ak5a9BKEYTCN0j1gwAB89NFHWLlyJRITE7Fs2TJMnToVDz74IABmVj5y5EhMnDgRy5Ytw6FDhzBs2DCEhIRgCA8eTBgWPiFz9hg9Vyby8oDr17WRiZDDTfFQYM7ADUv271dfHkL2AO1MCDe++Mq9ynsLJ07I+bJCDHIHcseOaSaOz8EtYMLD4bQFBfdXqmw7Qns+/RS4eVN+/eOPfjh1Spw8IuCL+gDQj7kcwcMPy+/9+qu+8hCls2oVS4sb2++7T84rwxYShBkwjdI9ffp0PPzwwxg+fDiaNm2K0aNH46WXXsL48eMLvjNmzBiMHDkSw4cPR/v27XH+/HnExcUh1NtmnF4I3727+27nvh8YCPjfOhyxc6c2MhFyuzhrgQCwXS0AOHBAfXl8ndxcICuL5Z1Runv3Zqm3WR1s3crSmjVR5vngW742SdlTEX79XfE1emt9HH/5tt8qXTlyBJg0ieVDQuRzy888I0ggQcyYwdJHHpHDLEZFyQr4t9+KkYu4nfx82fLg3Xdv/7xmTfYHAEuW6CcXQaiBaZTu0NBQTJs2DadPn0ZmZiZOnjyJCRMmICAgoOA7FosFY8eORVJSErKysrBx40a0aNFCoNSEsxw6xFJXlDvrLSs5Urq1gytrzih4HG5u6queorXk6lU537Zt2d/n99O24p2cmxauuAUHl/3ddu1YmpnpnfHKRcBNxIOCnP8Nd4C0ZYv68hDFM2WKnF+5Mg8dOyYBADZtKrz77c3k5srPMX70icN3u0+fpmNqRuGSIsiGcldbCd8B50cECMIsmEbpJryXW9HeAMAlBzvt27M0KUldeQgZbl7uymJI06Ys9bbdVSPAr2lEhGzpURpK8zwnnWuaAv6/OLPwoLwGdHZTHfh5er6g4Qy8rchzuX7Y7Szt2RPo1k3C8OH7Cj5buVKMTHrz559yvlevwp8plTp+5psQy549cr5CheK/wyOkZGT4zuIR4R3Q448QzsWLct4V5Y6bonuTMmEkJEneUXSlXfh3z54FHA715fJlXF0EUS5i8d96A/ye79mz7O+GhMhWF9624y8Kfv179HD+N507szQlBcjJUV0kogiXL8u7htwxVYUKOahbl5l7/Oc/ggTTmTlzWBoRUTiaAcB8lfDFy/nz9ZWLKB7eDkUdqCl56ik5T746CDNBSjchHL5716yZa7+rV4+lZ86oKw/BUC5mNGrk/O9q1JDztLOoLu6csefn37zJ8oA7gnLWMiYsjKVK00XCfRISWFq7tvO/qakI5X38uLryELezYYOcb9lSzt9/P7Oj9hVnatwSrqT5BbfM8pXrYXS45WNpY4vSeePJk9rKQxBqQko3IRx3TJiV39+4UV15CAZvF39/2SO5MwQEyCa93qToGQFuEso9cjsDv0+8Zac7N1dW+pwdM/jOCDne8RyloyNX+qHNJi+U0rigPf/6F0t79SqspLz6qnx4ed06nYUSAPeEPWJE8Z+PHMnS5cvJ54MRWLOGpcOHl/69AQNYykOIEYQZIKWbEI47zroAeYUaoIelFvB2qVrV9d/y39AqtLpw54Gu9HeumP7zj/ryiEC5W33HHc79hnvZ5jvehPsonfm1bu3ab6OjWUq7itrDrzH3fcJRWhwsXqyfPCJQOkdr2LD47ygjpij7NqE/yudakyalf5ePJRQNgTATpHQTwnF3p1vpIIniNaoPbxdXF0MA2as093JMqANfCOnf3/nf8LbYtUt9eUSg9JwdGOjcb/jZY9ph9Rx+DcPCXAsZBtC4oBfp6XL++ecLf2axAM89x/Jr1+onkwiUVnAlHZHi1hcA8NtvmopDlIFyYbh+/dK/+8QTLM3NlR07EoTRIaWbEI4751SBwhPuCxfUk4dguNsugLwKnZennjyEe1Yh/Iy9t7TFtWssdUXh431471715fE13LVMAuRxYfNm9eQhbke5wNagwe2f9+7N0n/+8e5QWfHxcj40tPjvWCxA9eos7+2LEEZHuRhX1vjevbucJ2dqhFkgpZsQjieTOO69mHaw1MeTdunWrXAZhDq4YxXSqRNLvaUt+P/Rr5/zv1F6wiVnap7hrmUSALRqxVLuTZ7Qhk2bSv9caVJ94IC2soiEm4sr/9/i4JYwGRmaikOUAXeK64yDTD8/5j8GAI4e1U4mglATUroJ4cybx1J3JnH8NzweKaEenkyu+W9+/VU9eQj3rA/4oom3KN28T7lyDSpXlvP796srj6/BHR25My5w5ef6dfXkIW5n2TKWchPcolStKofK8marg0WLWNqnT+nf4zv///sf+YcRyf/+x9J77nHu+3feydKZM7WRhyDUhpRuQjhcKeCmh66Qnc1SP+rJquOJeTlfqXb1zCdRMpLknvWBt3kv532KO5VzlbNn1ZPFF8nNZany3LCz8MWPc+fUk4e4HR7usbSwSzwiBQ/R5I1w03llGMviUJoqe8s4aUb4fE65SFoa/Dw+tRlhFkhVIYSSny+bgLnqCRcAHniApd6yi2ckPDEvb96cpRkZQGamejL5Munp8qTElYUQ/l1vuUf4BCsmxrXfPfYYS5cvV1ceX4Nf/4cfdv23yrGEFj+04cYNOWoE38EtjscfZ+kXX2gvkwgyM+XFh86dS/9u/frMMSPgPQ4nzQi3QurVy7nvP/00S73dNwHhPZDSTQglNVUeLN3ZUeWTOKXDFEIduFMZd9olNFQ2X0xJUU8mX4YrzYGBrlkQ8Hvkxg0gJ0d9ufTGXQsMHquYT64J9/DEAkbpzIrChmmD0gN0164lf69FC5bevKmtPKJISJDzJXkuV5KVxdIjR7SRhygdfv0B2fdDWSg3aujICmEGSOkmhMIncKGhslMMV+AKRU4OrXSqDbdAiIhw/bcWi+xdnuJoqoPS8sAVR1QVKsj5PXtUFUkI7lpg8HOd3rLjLwpPLGAAWRGkdtAGPt5Wq1Z6SD2lpYg3mudyT9gVKzo3Xg4YwFLy+SAG5bOJH30oC+Ui3s6d6spDEFpASjchFE+cdQFAu3ZynpuSEZ7DzZgBoGNH98rgyh61izrwSYmr94rS34HZPXffuCF7GHb1OvDvk1WMZ3ClxN0xm/9OGUOZUI8//2RpWYsiVavK+bK8nZsRfh0iI537Po9wQGHDxMCfTX5+ri0qly/P0u3b1ZeJINSGlG5CKJ6YKgLMVLRcOZb3xtV6UfB28fNzb6cbkHcWqV3UISmJpe4sYtx7L0vN3hbK3ZCS4u6WhDIMDXcGRriG8qiIMgybK3AfDzdueC4PcTt8wbRJk7K/y49aeOMuIe9nTZs6931ubu+OxR3hOfzZ5EooSEDu59wyjyCMDCndhFA8NVVU/tYbJw6i4O0SGem+Z3i+kLJ4sToy+TrXrrE0Ntb133qLMzU+sapb1/VYz0olhCZo7qG0lHB3obR/f5ampnosDlEMS5aw1Bnl5YUXWOqNYzQfL51V4nj4qcREwOHQRCSiFFasYKmr4wofTxYsUFcegtACUroJoXhqXg7Iuy9mVyiMhBqLIXzHwNUdSaJ4+L1SWhigkvCWWN38GnDv+K7g7y9bbZh9x18U/Lo1bux+GdWqsZQcVmkDH2+dsUTgobSUTqy8hcOHWcr7W1nUry/nT59WXx6idLijy7w8137HQ816g5NQwvshpZsQihrKHQ8bYXaFwkiosRjCw35Qu6iDJ0cxvCVWt6fHUbjSvXmzOvL4Gp5ef0A+Y3v8uOfyEIXJyZHN9rm5dGnw+NTnz7uu7BiZ/HxZcXb2THdAAFC9Ostv3aqNXETJ8LGF71w7C3fMmJpa2BcNQRgRUroJoagxieMKuzc6gxGFGu3iLYqeUfBkgcpbzMuddRJVEnxHLz1dHXl8DTUW45RhfrhTPEIdlM6olFELSqJtWzlv9rFBCTctBwr/j2XBx4dDh9SVhyib9etZ6urYrlxcOn9ePXkIQgtI6SaEosYkjjuDSU72XB6CoYYFglLRkyTPZfJ1PLlXvMW8/MIFlrrr7GjQIJaa/TqIQq1xwd+/cHmEOvAwWX5+gNVa9vcDA2XfCN4U2lHp3yUkxPnfceefJ06oKw9ROsodam4u7ix+fnIb8/5PEEaFlG5CKGpM4tq3Zymd6VEPNRZD+G8dDtrtVgM1drrN3g58ctahg3u/59du2TJ15PE11LCAsVjkdqBz3erCLUGqVHH+N7wtvMmkmjtKdDXyRsuWLLXb1ZWHKB3l4pszxyKKwvv7li3qyEOUTVIS0Ls30KkThX90BVK6CaGoMYnjjqVo10Q91FgMUe4w8Ni+hHvk5srenj05060M+WRGeL90N1wV3yGnsEDuocZiHCCb/yYkeFYOURh+bELpFKwsGjRgqTeFcOPjBPcr4izNmrE0OFhdeYjS4eNK5cquR6UA5D5Mx1X0Y8ECFtN+xw5g8mTR0pgHUroJoai5o3rtGsXfVQs1FkMA2cs0hWjyDOX1cyduOl88uXzZvBMTSfJ8vLjnHpYqz3wSzvPTTyz1ZDEOAAYMYCm1g7rwceKBB5z/Df/uzz+rL48ofv2Vpa72044dWZqaSn4f9GTbNpa6O67cfz9L585VRx6idBYuBN58U369ahUwYoQ4ecwEKd2EMCRJnR1VpXdSmsSpg1o7Wo0asZSsEDyDX7+ICPk8rCsow+aY1Wv0jRvyopq7/ZL/7swZdWTyNaKiWOrqucuicOukkyc9K4cozIEDLHWlfXibZmaqL48o+E61n4szXOV1O3pUPXmI0rl+naXuLs5zr/O06aIPX31V/HveFAFBK0yldJ8/fx5PPvkkKlasiJCQELRu3Rq7d+8u+FySJIwdOxbVqlVDcHAwevTogcM8WCNhOG7cYOd9Ac+UO39/oHx5llc6UCHcR43FEMB7zhKLxlPLAz8/2erArAsgvA8FB7vmHElJ5cpy/tw5z2XyJZSLpEoP5O7APWuvXetZOURhjh1jqSvWMJ07szQ727xWMEXh/TQmxrXf+fkBTZuyPEVD0Q/eXkOGuPd77tcHkBV4Qhtu3pTPzn/zTeHFKbI0KBvTKN3Xrl1Dt27dYLPZsGrVKhw5cgSfffYZKijiYkyZMgVTp07Fl19+iZ07dyI6Ohp9+vTBDW86rORF8IHWk0k0h4f6IOVOHdQyL+e/J0cbnqGG5YHZPZjzuLueXIPwcDlP54ldQw1LA06tWiz1dNwnZJSORNu0cf53jRvLeX6PmR3uxdqdfpqfz1JuNUBoD198c3dc4eMJAJw967k8RMls3iznH3qo8PjxySf6y2M2TKN0T548GTVr1sSsWbPQsWNH1KlTB7169UL9Wx5DJEnCtGnT8M4772DQoEFo0aIFfvrpJ2RkZGD+/PmCpSeKQy3FDgAefrhwmYT75ObKZvqetk1gIEtpMcQz1AzhZta22LuXpa6ajBalUyeW0ljhGrzfhIR47miqVSuWUhuoBzfN9fNzzXu5xQKEhrL8rl3qy6U3aWlyvmZN139/110s5eEJCe3hTkI9GVe48kdjirbw3eyICNnx3aRJ7L0jR8jEvCzcOB0ohuXLl6Nv37545JFHsHHjRlSvXh3Dhw/HCy+8AABISEhAcnIyYhT2RIGBgbj77ruxdetWvPTSS8WWm52djWxFkMC0WyO2w+GAg9s+Gwwul1Hlc5bkZAsAf0RGSnA4PDuMExHhB8CKFSvyMWKEb931aveHS5cAwAYACA11wJNi27dnbZyR4Xkb+zIpKax/R0Tkw+EouX+X1hciIqwA/JCSkgeHI18jSbUjLY1dgzp1Sr8GZREZya7D2rV56N/ffNfBFdQcGy5eZPdyxYqe38thYQBgw8WLQGamwy0/BURhDhxg7RMRISE/P7dgx5ZTWl+oXt0fx45ZcO2aOccGJezYCHt+Vavm+vOrbVv+zPJsnDE6RppHZmb6A7CgQ4dcOBySW2XwcX3Tpjx07WruPqw3rvSFBQvYvfX44/JY8eyzwNtvs/fj43PRq5d7bWhmnL2PTPOoO3XqFL755huMGjUK//nPf7Bjxw689tprCAwMxNNPP43k5GQAQBT3CnKLqKgonC7FZmrSpEkYN27cbe/HxcUhxOC2b/Hx8aJF8IiNG2sAaAfgMux2z4KEJibeAaAurl69DLt9mxrimQ61+sO5c+UB9EK5cjmIi1vlUVmnT4cCuAfJyTmw2/9QRT5fZMeOZgAaIi3tFOz2sv1UFNcXUlObAmiEPXsSYbcfUl9Ijdm9uwWA+qhU6STsdvcDPJ850w1AJRw/fhZ2u2/EslNjbNizpwqALrDZrsNu9+y8SG6uBQBzOfztt3+iXj06iOkpa9fWAtAGV65YYC8l0HRxfaFWrVY4dqwOtm//G7Vr/62hlNpz/HgEgLtQuXIG7HbX+/2lS5UAdMOpUxmw273f6YDoeaQkAZcu3QfAimPH1uHqVfc8+p071xNAGPbuPeXR88GXKasvZGf7AWChJ6pX3wa7XWlWwMIgfPNNIrKzfc+XVoaTDjFMo3Tn5+ejffv2mDhxIgCgTZs2OHz4ML755hs8/fTTBd+zFAnyJ0nSbe8pefvttzFq1KiC12lpaahZsyZiYmIQxpbjDYfD4UB8fDz69OkDm80mWhy3OXWK2Yk2alQRsbGxHpZmwapVgJ9fZRXKMhdq94c//2T3S3S0zeNrmZwMvP46kJ4egL59Y2G1eiyeT/Lbb+zCtW9fF7GxtUv8Xml94fhxPyxdCpQvXxexsbVKKMG4/PILuwYdO9ZDbGwdt8s5dcoPo0YB5cvXQmxsdZWkMyZqjg3XrrFxoW7dMFXG2OrVJZw/b0HTpnf65M6I2hw7xp6nDz6YX2z7lNYXtm71Q1wcsGdPE/z0UwNd5NUO1k9r1gxyq59Wrw68/z6QlFQeffrEwsRTrFIxyjwyPR1wONjY/vDDPQuc4rrKrl1+mDAB2LmzARYtqqOegD6As31hxQpZlxo9ulOhmOpjxuRhyhQrli9vgCVLSp6jeCtpynMtpWAapbtq1apo1qxZofeaNm2KpUuXAgCib8V6SE5ORtWqVQu+k5KSctvut5LAwEAE8oOnCmw2m+EVWjPIWBr83HDlyn6w2Tw7qMmb+OpVi6mviSeo1R94fNKICM+vJW+X/HwL0tNtqpzf90X4vVKlihU2W9krF8X1BX7O89o1z+83EfAzq85eg5LgYYHMeh3cQY2xgXsFrlRJnetWvz5w/jxw/bq/1yo2esLDr9WqVXr7FNcX+Jnu69fN//zkZ7Hd7ad33CHnL12yFYS381ZEzyP5uBIYCFSoYEMpe2SlwiNTeEMfFkVZfWHfPjkfEFD4e926yXl/f/fb0aw42+dMM+Po1q0bjhcJMPv333+j9q0RsW7duoiOji5kHpGTk4ONGzeia9euuspKOIdaYamUZZATDc/hzv7dXXFWEhAgO0dRRPcjXEQNp4Nmv0fUGi/Mfh1EwR2pqTFeK8uhdlAH7lXYHQM97jwsOZmZ+5qZ9etZ6q6hYkCArMBt882TarqifLZ5oqhxpU8ZipZQlw0bWPp//3f7Zz17ynkeUoy4HdMo3W+88Qa2b9+OiRMn4sSJE5g/fz5mzJiBV155BQAzKx85ciQmTpyIZcuW4dChQxg2bBhCQkIwxN3gf4SmqOm9nJdx86YcPoxwD650890PT+EPQLN6zTYC3HJJGfLKVczuvVyNsGnK3x886Fk5voaa47WyHGUIGsJ9goJYWsuNkyPt2sl57knarPAjTIposi7DQ+M5aTFKeACP8+zpuMIjIgC0kKcVe/awtDjjYeV88ddf9ZHHjJhG6e7QoQOWLVuGBQsWoEWLFhg/fjymTZuGJ554ouA7Y8aMwciRIzF8+HC0b98e58+fR1xcHELV0h4IVVFrEg0UVka8IeyJSNRWugcNYqlZlT0joEab8PvMrBMStZQ+ZRihixc9K8uXUFvp5soRi5ZAeAofX5XKh7OUKyfnd+5URx5R8OvAd+/dYeDAwmUR2nHsGEs9tbDw9zf/wrLR4RsoHTsW/zl3oaA0QycKYxqlGwDuu+8+HDx4EFlZWTh69GhBuDCOxWLB2LFjkZSUhKysLGzcuBEtWrQQJC1RFmqal/v5yQqJWZUKo3DzJkvVUrrNruwZATWUbn6f3bgB5OR4LpOeOBzyrpOn4wU3HQVI6XYFtc3Lu3RhqZNOX4ky8HRRRPb5oI48olDzKM6yZZ7LQ5QO9yHTsqXnZfF2o6Ns6nPzpjxv6NCh+O889BBLN2ww/zEVrTCV0k14F2rvnHTvzlJa5fQMbl7oiSmzEv4gpB0t91HjnH2FCmxxCjDfAgiX12LxzGyU07QpS2mscB61x+s6dVjK4ioTnpCRAWTeirTk7qLInXey1Oz3hBr9lFth+NEMWXN4f2vdWr2yUlI8L4sozIEDcj4ysvjv3HOPnC8lUrNPQ0MKIQy1d074Q5ZWpz1Dq7Obc+aoU56v4XAA2dks78lOt58fEBHB8mZVuiMjoUrYObK+cB2txoWzZ81neWE0eNv4+7s/RvD2WLJEHZlEoYYFXZ8+LN2xg3bstEZNi8fHH2fpL794XhZRGO5ErWnTkhej6tRhXugBYPFiPaQyH6R0E0LIzJRX5tWaxHHPl9xbNuEeap61B2Rz3lIi9xGlwM09LRb3PfJy+MTGbLtZavdJfh1OnVKnPF9A7UXSxo3lPO12ewY/JlGpkvseoP1vBZA18y5hVpZsruzJWKGMTkvHH7RFzcW8kBCWqmWlR8icPcvSshahuCUaOSotHlK6CSGosTJflHvvLVw24R5q72hxz7jULu7Br1uFCvLE2F3MusOrdp/kYw6FNnGOjAw5KoRabeDvLzu1M1t/NBq8H3vyLOUhf44c8VweUSgdOHmieEVFsdBhAPVNrVFzbOfmzWZbVDYDf/3F0gceKP17L7/M0h07tJXHrJDSTQiBO0UKC/MsNqMS8lypDmqaewFyu1y/TvEz3UHNXV6zKt38ga9Wn+TWF2qYqvsCvL8EBHjmV6AoNGarA7cas9ncL6NtW5b6+5vXpDo5Wc57Mq+wWOS+efKkZzIRpXPiBEvVGNt5m+3f73lZRGH27mVp7dqlf69BA5YeP66tPGaFlG5CCGqHpQLMq1AYDbVNeSMi5AnQ1avqlOlLqLkTYFbzcm4+m5enTnncczaNFc6hHBPUWiTl5QHUDp7Cr1+/fu6XUa0aS3NzzRufml+H/v09L4uPNYcOeV4WUTxKZ1u8/3mCMkY9OW5Vj/x8Od++fenfVX7OraMIGVK6CSGoHZYKkBWKCxdoR9VdcnNl7+VqxuPlDrwOH1anTF9CzbO0ZlVy+CJd167qlEc7rK6htnk/h/fpP/5Qt1xfQ40xIjhYPhNr1l0qNReMeQgrPvYQ6qO0TPDUXwkgh70DaLdbTficEABatSr9u0pfHVu3aiKOqSGlmxCCFjvdypXSY8fUK9eXUO5ElxQWwpNyz5xRr0xfQU2Fx6xKN5eXm655Cr8OFNbEOdR2osahnRB1UGuM4E7DEhI8K0cUao6Vbdqw1MyO5YwOby9+tEFNuOMvwnP4+B8aKvs6KAmlJRQ3SSdkSOkmhKCF0m2zyeZFZlMqjIKaTruUDB5cuHzCeci8XDvv5ZmZzNcAUTpa7XQPHFi4fMI91Gqf++8vXJ7Z0MIq6OefPS+LKB61x3VADhtm1j5sRLZvZ6mz7cSdqVHYsNtxS+nevHkznnzySXTp0gXnz58HAMydOxdbyBUs4SR855OHF1AL8obrGVwBUbtdeLgwsyl7RoDMy9V37le1qpznjnyIktHavNxs/dFobNvGUk/bh4/TZj3HrMUCJXe6SKiP2uM6ILfXgQPqlenrcPNyZxeo+WZaUpIm4pgal5XupUuXom/fvggODsbevXuRnZ0NALhx4wYmTpyouoCEd6LFYAvQWU1P0cICATCvsmcE+AKVGub+ZmwHSVJ/R8Rikc9smulaiEIr83Iarz1HGUc6Otqzsrhn+j17PCtHFGrOKzp0KFwmoT5aLObxPkxnutWDt9Njjzn3/e7dWUrHCW/HZaV7woQJ+PbbbzFz5kzYFPEpunbtij1mHakJ3dFq54SXt369uuX6Clop3bSj5T7ck7AncWc5ZjQvv3GDOfgD1B0vqE86j9bjdUKCecNUiUbpjErpxMgd6tVjKQ/RZza0CK+YklLYezOhHlqYl/N7QM0oC77OqlUsdbadlB7Mc3LUl8fMuKx0Hz9+HHfddddt74eFhSFV6eKOIEpBq50TDnkcdQ8tvMoDtKPlCWouhPDd8tRU80wkucKn9K6sBtQnnUcrpVvp/PLUKXXL9hV429So4XlZ7drJeTMugqjZT5WesMlUWRu0sHhs2rRw2YTn8GhAZTlR4yjvHdqLLYzLSnfVqlVxophDcFu2bEE9vkxKEGWg1SSuZ0+WmjXOqGi0Ni/ftEndcn0BNduElyFJQHq65+XpgRa7Icry/vc/dcv1RrRaJOWmoIAci51wDTUVl9at5bzZnqH5+fJRHDWuRUAAW+gDKOazVmgxtvOyzp2j0LFqwceCu+927vtWq+wXiFx9FcZlpfull17C66+/jr/++gsWiwUXLlzAzz//jNGjR2P48OFayEh4IVop3dWrs5R2TdyDG6uoYcqspGFDOZ+ZqW7Z3o6aSndICOB3a9TnVg1GRyv/D9z80NnVe19Gq/EaoLOznqKm4hIcLCuaZmsPpfWOWv20Y0eWmu1amAUtxhWlxcfff6tXri/D59Pc0aIzcKWbLMkK47LSPWbMGAwcOBA9e/bEzZs3cdddd+H555/HSy+9hBEjRmghI+GFaLV7xSfmFy7QWRJ30KpdeCg3gCYwrqKm0m2xyLuLZjmCoZXCd++9hcsnSkZLpZuXuXKl+mX7Amq3DS+HhwkyC/w6lC+v3kIaWcNoixYLqgEBQJ06LL95s3rl+ipKpdmVdhoyhKWffaauPGbHrZBhH330ES5fvowdO3Zg+/btuHTpEsaPH6+2bIQXw81V1A5N1by5nFc6mCGcQ8tdRX7Oh5Qc53E4ZOU4IkKdMrnybhalWyvTZnKk5hw5OXJf0cIHBzcBtVrVL9sXUHvM5mGBzOaiR6tnFwAEBqpfpq+jRVQKTlYWS81yhMrIKE8Tu9JOfC7OnaASDLeUbgAICQlB+/bt0bFjR5RXHswiiDKQJO0cdlmtcgxeMmtxHS13tMzoOVs0/IyixaKe0h0WxlKznNnU2nM2Kd2lw6+Pn5/6x04AYMAAltK44B5qKy48LJDZ2kMLBa5fv8JlE+qRni5bI6o9tg8ezFJqN8/h11DpkdwZeveW8+SvQ8bf1R/07NkTllJ88a9bt84jgQjvJyNDPnulttINsAE8KYkm0+6g1cqzsswdO4BevdQv3xvhfTgiQr2dQO7BnCv0RkdrR2qpqWw13t/lp6FvwK9/ZKTsD0BNaPHDM9Te4eXtYbcD77+vTpl6oMXiHB190A7eXoGBQLly6pbN223FCmDSJHXL9jXcva+UHsz37QP69lVNJFPj8jSjtdK9JQCHw4F9+/bh0KFDGDp0qFpyEV4MN1W0WNQNAcShHVX3uXaNpVwxUxNu8kWrns6j5UTSLPeHVn0yIoKNQZLEFiCUkwRCRssxAaDx2lPUHiP44pNZQgpytDAvV4a0kySK/awmysVUta8rHQdQD0+Od1WowBa1//mHlG6Oy0r3559/Xuz7Y8eOxU2zuMMlhMKV7vLltXmI8cnHqlXA44+rX743w9uGmyCrSe/ewM6d8plBomy02OU121lmrfqkvz+bFFy7Bhw6BNxzj7rlewtajgmA3Lf372eKnha76d6M2mNEz57AhAnmG6d//ZWlao6Vd9wh59PStDle4atoeZSNh7Y6fJjGFE9Ztoyl7rRT9+7M2sBuB8jPNkO1rvjkk0/ixx9/VKs4wovRKiwVJzubpbQq7TrKBRG14R5FzaLsGQF+r6h1nhswnzmvln2S7+KeO6d+2d6CltcfAJo0kfMpKdrU4c2ovcPLwwKZLdwSt5pTU8EKDpbLJUsMddHS8V2zZnKeYqx7Bg8h6M59xduWWzkSKird27ZtQ1BQkFrFEV6MloMtAAwcWLgewjny82Vvn1qctTfbDqsR0MLhoNnMy9UMmVYUszqN0hMtrz8vl++i09jgOmrvGCqfy2ZqD34Px8SoWy4/VrFtm7rl+jpa+o9Rjik0tnuGJ/cVD8u5fr168pgdl83LBw0aVOi1JElISkrCrl278N5776kmGOG9aDnYKsuNj9emfG9FeTpEKwd3ALB1q/pleytaKDxmW/zQUukz27UQgdZKNwBERzPz3dOnC4d9JEonIwPIzGR5tZ6nfKcbYOa5d92lTrlao5W5Mn8ums3c3uhovflCY4o6eHJfcSumwEDyicBxeac7PDy80F9kZCR69OgBu92ODz74QAsZi2XSpEmwWCwYOXJkwXuSJGHs2LGoVq0agoOD0aNHDxw+fFg3mQjn4DexVo55KldmaU6O+ZzBiIRPrq1WQAujFaVTGoqf6RxaKDxmNS8npVsMeijd3PHRjh3a1eGN8H5rs6nbPvXrs9RM7eGJw6fSePRRltIYoS5anukG5DFl505tyvcVPFkcqVePpdnZbIGQcGOne9asWVrI4RI7d+7EjBkzcIfSywWAKVOmYOrUqZg9ezYaNWqECRMmoE+fPjh+/DhCtZwxEC7BJ3Fanelu107Op6UxZ0lE2SgfglqsSDZsKOdTUoC6ddWvw9vQ4jytmZRuSdJ2csbLXLIE+PZb9cv3BrSeHANsV+rgQdkfB+EcWo3ZlSsDJ0/Ku+hGJzNTntRrFVpwxQpzhVAzOlpbPNKY4jme3lflygEBAWwDLDGRLA4AFc9068XNmzfxxBNPYObMmYhQeBeSJAnTpk3DO++8g0GDBqFFixb46aefkJGRgfnz5wuUmCiK1jsnQUFy3Ec6z+M8Wj8EAaBGDZaaQeEzAtykUU3P0WYK0XTjBouhDWjTL/l1JXckJaOH0t29O0vN0CeNhFZjdo8eLDXLOM0dIvr5qb+Yz0OokWmsumhtXk5jiufwNvL3d28OYrEwhRtgEUIIJ3e6IyIiYHFyxLl69apHApXFK6+8gv79+6N3796YMGFCwfsJCQlITk5GjOK0f2BgIO6++25s3boVL730UrHlZWdnI1uxFJaWlgaAxR93OBwa/ReeweUyqnxlcf26HwArQkLy4HBoY/9dsaI/0tMtuHgxF7VrS5rUYRTU6g8XL1oA+CMyMh8OR54Kkt1OZKQ/zp2zYMOGPLRqRbb/ZXH5shWAHyIicuFwlN2PnekL7OFpQ2qqhKysXFit6siqBUlJAGBDSIgEf/9cqD3kdejAyk9NleBw5KpbuAFQY2y4dIn1wfBw5/qgO1SowJ4Jly5pN/Z4I3zMrlix7OvmSl/g7TF3roRPPjH+fcGmnTaULy8hN1ddee+6i13ja9e8a4wQPY+8fNkfgEWzcYX34TlzJHz1lfe0mxaU1Bd27eLji/v3VZcuVmzb5oc//sjHoEHeO7Y7ex85pXRPmzbNE1lUY+HChdizZw92FnNIIzk5GQAQpfQCcuv16dOnSyxz0qRJGDdu3G3vx8XFIYTHijAo8Sb1FHbsWFsANXH+/FHY7Sc1qcPf/24AFWC378Llyxc1qcNoeNoftmypDaA1cnIuwm7X5jBfUlIfACHYs+dv2O0mi0kjgJMn7wIQgYSE3bDbk53+XWl9IS/PAuB+SJIFixevQVhYjueCasQ//1QAcDdCQjJht6s/3t286Q+gP9LTLfjttz8QEOCdC0GejA2nTrGxNDFxF+x2bcbSM2eqAeiA3bvTYbev06QOb2TTpjoAWiEnJxl2u3OHV53pC2fP1gLQBkFB6bDb13okox6cOBEOoAdstizY7XGqln32bCiAe3DihAV2u13Vso2AqHnkuXNsLnD8+FZI0jXVyz97lo0pubmSV7abFhTtC2vWsHHg4kX3+35WVmcAUfj77yTY7bs8F9KgZDh5aN0ppXvo0KEeCaMGZ8+exeuvv464uLhSQ5MV3ZGXJKnUXfq3334bo0aNKnidlpaGmjVrIiYmBmFq2nSqiMPhQHx8PPr06QObzSZaHJf54Qe2tdahQ1PExjbWpI4vvrDi1CkgNbUDYmO9cyLNUas//P03O21Sv34UYmNj1RKvEMOG+eGTT4BKlRohNraBJnV4E6NGsSE6JqYdunZ1bqfbmb4QHi7h+nULWrfuXShOstFYvZqN3dWrB2vSJyUJGDpUQl6eBR063Ivq1VWvQihqjA2vv876YN++7dGpkzY73YGBFnz6KXDuXKhmY483smcPG7ObN48u87q50hdq1QK++grIzi5nivbYuJGNE5UqBaku76VLwKuvsnzHjrGamUPrjeh5ZEYGG1cGDOhS4LhPTZo1Az75BMjL80P37rGaOoI0OyX1hcOH2fjy1FP5bt9Xp0/7Ye9e4MCBaqYYS9yFW0mXhcuO1JRkZmbetqWulaK6e/dupKSkoJ3CS1ZeXh42bdqEL7/8EsePHwfAdryrVq1a8J2UlJTbdr+VBAYGIpC7OVRgs9kMr9CaQcbiSE1laaVKVths2ti28nUWq1W7OoyGp/2BL9SFh/vBZtPG3UOVKiy9etV32sUT+DhesaI/XGnasvpCpUrsvHhams2lcvWGjxWVK1s0G+sqVmSO/dLSbKhTR5MqhOPJ2MDP9UVFudYHXaF9ezmfm2tDcLA29Xgb/P6oUsX5MduZvsCnUFeuWODnZzP0ERRAdvgWFqb+OKGMunH2rA2K6aVXIGIemZUlRzCJjtbmGdRAsaaflGTTLFqON1G0L3BfCa6ML0XhDnRv3tTuGW4EnP3fXL6K6enpGDFiBKpUqYLy5csjIiKi0J9W9OrVCwcPHsS+ffsK/tq3b48nnngC+/btQ7169RAdHV3IPCInJwcbN25E165dNZOLcB2tHWgAAF9QM4sjGCOgR2ggM3nONgJatYlZ2kEPJ15muRYiyM6W4xRrOV5XrCg7rKJ2cB6t7g9leQcOqFu2FvDFSa2ME1u3Zin1TXXg19Fq1S6KjcUiK3zUbu6hxvjSpo2cJ6d2bijdY8aMwbp16/D1118jMDAQ33//PcaNG4dq1aphzpw5WsgIAAgNDUWLFi0K/ZUrVw4VK1ZEixYtCmJ2T5w4EcuWLcOhQ4cwbNgwhISEYMiQIZrJRbiOHhNpPkFcsEC7OrwNPrnWUunm7fLHH9rV4S04HHK4E7XbhJfHlXqjoudYsXWrdnWYFX79tfAKrcRikdvh8GHt6vE2tPJerty0uWgClyi8n2q1m8n75ubN2pTva2gdnpTD223bNu3q8GbUeP5GR8t5MyzgaY3L5uUrVqzAnDlz0KNHDzz77LPo3r07GjRogNq1a+Pnn3/GE088oYWcTjFmzBhkZmZi+PDhuHbtGjp16oS4uDiK0W0wuMmKhoYRqFyZpQY9lm9ItAhPVRQeMgwA8vPZZJ4oHqVC7KtKN1cqtNxl5btk19T35WN6+DWpUEH7e5XXlZiobT3ehJZWY/fcA6xbZ45dQq2t5/iCtJPHNoky0CM8KSC3l8ZBlbwWtZ6/ViuQlwecOsXGFV/G5cfo1atXUbduXQDs/DYPEXbnnXdi06ZN6kpXBhs2bCjkWd1isWDs2LFISkpCVlYWNm7ciBYtWugqE1E6OTly3D4tlTt+9D8tTY7zS5SOHg/C5s3lPD+PSBQPV4gDA6H6mTezKN167HQ/8ABLSem+Hd4/9Fi8HDCApdQOzqPl/cEn2itWqF+22mg9TtAYoS56jOsAMHAgSxcu1LYeb0WtduJjuxnGEq1xWemuV68eEm8tRTdr1gy//PILALYDXqFCBTVlI7wQ5SS/fHnt6uFmZpJED0pn0eOsfUCArPBduqRdPd4AX5TQYlg1i9Ktx0IQd5Rkhh09vdHDzwOndm2WnjihfV3egpb3B1+s9vfI3a4+aD1OcBPZQ4e0Kd/X0GOuAcj9gTttI1xDLaW7XDmW0lzcDaX7mWeewf79+wGwcFv8bPcbb7yBt956S3UBCe+CT+KCgrR9mPv7y0r9Lu8NDagqWp+L43Alks7Qlo6WE0muRBndXFKPyRm/vuTk5Xb0VLr5uLDW+GGhDUFOjtw+WtwffHfKDItRWo8T/CgcnUlVB73My++8k6VXrjAfKYTz5OfLZvmetlPfvizdssWzcrwBp9WekSNH4vnnn8cbb7xR8F7Pnj1x7Ngx7Nq1C/Xr10erVq00EZLwHvScxPEwIjSZdg6ugGnpMAmQjxcYfZdVNFq2B59EGn3lWU/v5X/+qV0dZkXP8bpmTZbyXRGidPiE2M9PG2sYfl+Yweml1uME915usbCzqUYPoWZ09NrpVnrOvnKlsFMvonRSU5niDXh+X/FjhZLkWTnegNM73X/88QdatWqFjh07YsaMGQWBwGvVqoVBgwaRwk04BVck9JjEPfIIS82wUi8aSdJvgv3ggyyldikdLduDT3aMviClx46I0rkfmSEWRk+lm08haFxwDn5vRERo4+QuKkrO5+WpX76aaD1O8CModFxNHfQ60+3vL1vu0bjiGvyeCg1lxwI9gYduA4CEBM/KMjtOD9XHjh3Dpk2b0LJlS4wePRrVqlXD008/rbvzNMLc6DXYKusg5w1lk5UlT6y0nmBzhY8egqWjpcJjhtjUGRmsXwLa7og0aCDnyc9AYUSM18nJ5PzSGbTeLeS7uwBw4YI2daiF1tfCZpOdCf79tzZ1+BJ6mZcr69i7V/u6vIlbp4hVaSPlHMboY4nWuLQ+2q1bN/zwww9ITk7G9OnTkZiYiB49eqBhw4b4+OOPccHXryZRJnqEAOJwUxYyBSsbvRzcAfIgTgpO6WgZN90MSjeXzWbTtk9aLORMrST0nBwrTT/JYVXZaL0gotzdMnJ7ZGayBTpA237KrfROntSuDl9BL/NyZV3JydrX5U1wdU4ta7iOHVnq689Yt4ySQkJC8Mwzz2DTpk34559/8Oijj2LKlCmoU6eOyuIR3oaeOyexsSylGI1lw5XucuW0j8fL2/5W4AOiBHzdvFyp8Fks2tZF1hfFo+fk2GajxQ9X0GNBhE+UjfwM5X3F31/b0Hb3389SspzzHD3ngfyY4eLF2tflTfA2evRRdcrjbb1qlTrlmRWPptfp6enYuHEjNm7ciNTUVNSvX18tuQgv5fp1luoRXY7f5Lt3a1+X2eHn1PRoFx4aSGuHbWZHyzbh90ZqqnFNeUWMFWfPal+XmdBzpxuQTf3PndOnPjOjx4II30e5FSXWkCijbmi5OBcYyFJPz7cS+o4rfNGazuK7Br+v+EKop2RnszQlRZ3yzIpbSvemTZvwzDPPIDo6Gq+//joaNWqEzZs34+jRo2rLR3gZejrmUTqCIU/ZpaNWaAhnaNaMpdevUxiP0tByYqIMC2fUyYieYwVfAFq/Xvu6zISeO1KA3A7r1ulTn5nRo23McF/odWSNW84Z2TrIDOTmyguqeljQ9OjB0n/+0b4ub0LtRb3+/Vnq61FCnFa6z507h48++ggNGzZEjx49cOzYMXz++edISkrCjz/+iG7dumkpJ+El6DmRVp52IHcDpcMfgnrsPkdEyDsSvr7qWRpaTqr9/eUdZKOa8uo5VvA6yP9DYfRWunm4sH379KnPzOixW8gn3Ebe3dWrj/JrsXq1tvV4O3yB32KRQ1dqiTJsGC3yO4/a4wv3YH7xojrlmRWnle46dergiy++wAMPPIDDhw9j27ZteP7551Fea69LhFeh50TaYgH4iQdanS4dPdvFapWd3O3cqX19ZoVPTpS70mpidGdqevZJvhtC40RhtO6DRenUiaXciSBRMnqYl3fpwlIj3xd6Kd1KR39GD6FmZHhfqlBBn0VOpcUjHTV0HrXvq3bt5LxRrev0wGml+5dffsH58+fx6aefomnTplrKRHgxWnpkLg4+YPi6SUtZ6KngAPK5br7DTtwO95arlXMg3tZGPXqh51hh9AUIEeTny31DSwdVSrp2ZSn3Rk2UjB7KJi/byIujepmX8zjyAPOFQbiHns4ZAabYBwWx/Nat+tTpDajdTsqz4UaOhqA1TivdgwYNgr+/v5ayED4Af1jpNYnjK2pcgSGKh0+u9TJc4adRjLyDIhqtF0KMrnTrOVbwicX589rXZRbS0+W8XotxVauyNDlZtoYhikcPZZObhALG9WCu1063MlY3PbfcR+8jK4A8rtBiifNoeXzFyI4ZtUbj4EAEURi9VzkHDy5cL1E8eu908/ansGElo/VOLy/XqKa8eo4VfGJx7pzsZdXX4f3Czw8ICdGnTuUE7/Rpfeo0K3ooL5Ury/n9+7WrxxP0VOL4WLR9u/Z1eSt6R0QAgMcfZ+mCBfrVaWYyM9kfoG47PfggS//3P/XKNBukdBO6omcYIACoUoWldJandLhFgB6OTQDZMc+lS/rUZzby8mQTW1/d6dZzcsZDVQEUNoyjtH7ROk46R6ncnzypT51mJC9PHrP1Ul6M6oxUzzkF3+2nHVP30XvjBZAtFGjzxTn4dfL3V9fSzGZjqS87UyOlm9AVvXdU+YBx8KA+9ZkVPUOGAcDdd7M0IUGf+syGcvdZa6XbqEcv9NzB8veX/QzQxIyh91jN6d6dpdQOJXPtmmx+r7WTu0ceYalR20PPfjpkCEuNei3MgAjz8jvvZOm1a8xXBVE6ygVvNRdc+/RhqVGtZvTAaaU7Ly8PBw4cQCa3OVCQkZGBAwcOIJ96M1EKubmyyYpeE7nWrVmalaVPfWZFa6ddRVF6sqQwHrfDJybBwbITGLXhk3Wjn9XUa0eE10MLdAx+/fXyXM7h7bBtm771mgneNuHh8u6RVvD2MGqsbj2Vbn4t4uK0r8tb0cvxnRLlfIPO45eNVgsjLVuy1KjWdXrgtNI9d+5cPPvsswgoJmBjYGAgnn32WcyfP19V4QjvQrl7p5fDLh4yDCBzxdLQe1erShV5BdWoSp9I9AjVZHSP3Xqf/eNhgGicYIgwAwXknShfNkEsCz1DufndmiUaNdKEns8uPv0l7/ruI2KnW7lwTUcNy0arsb9JEzl/5oy6ZZsFp5XuH374AaNHj4a1mMB6VqsVY8aMwYwZM1QVjvAu+MPRZgMCA/WpU6nck/OTktFb6bZa5fPjhw/rU6eZ0CNUE5/0GHHlX5L0n5zx3RCjOpbTGxGTY0A2BV2yRN96zYSeody4uT/tdMt98+BB8q7vLqLGlZo1Wbppk771mhGtFrzDw+W8kcMQaonTSvfx48fRuXPnEj/v0KEDjh49qopQhHfCV8qVN54e8IcxOe0qGRHnN/luDTmuuh092oOvYhtxpzsjQz4SotdOa/PmLDXi9RCB3s4VOdw6Sa+FWTOiZ4jHpk1ZarEY8zysnvOKFi3kPFlouYcI83JAdqpLVgplo6WVE7cWSU5Wv2wz4LTSnZ6ejrRSPO7cuHEDGdSbiVIQESoCAJ58kqU0mS4ZveN0AxTOrTT0ULqNbF7OZbLZ9OuTRt75F4EoR2rt27P05k3fPvtXGnq2DTcJlSTjee3OzJSVKD3mFRUrMqeLgDHHTTMgaqf70UdZOm+evvWaES3b6KmnWOqrlkxOK90NGzbE1q1bS/x8y5YtaNiwoSpCEd6J3uHCODzW6J49+tZrFvLz5bbRc1eLrzz7sifLktBzp9uISqZy90qvcFV8grF5sz71GR1RSneNGnKeztcXD9//0OscM68nJUX7+lxBq9BGpcHNlM+f16c+b0OUxSOfB5KFQtlouUHGLZiSktQv2ww4rXQPGTIE7777Lg4cOHDbZ/v378f777+PITyeAkEUg6hJHN8pO3RI33rNwvXrstmgnqvPvF1I6b4dPXe6U1NlJ2JGQc8zqxw+KcvKYpEWfB1R47XFAjRrxvK0m1g8ercNH6tL2XcRglahjUqDX4s//9SnPm8iO1uOVqL3uKI8HUtGuaWjpXl5jx4s/ecf9cs2A04r3W+88QZatmyJdu3aoV+/fnjjjTcwatQo9OvXD+3bt0eLFi3wxhtvaCbopEmT0KFDB4SGhqJKlSoYOHAgjh8/Xug7kiRh7NixqFatGoKDg9GjRw8cJi9NhkHUJI6fw/JVb4llwVeeg4Lk8zZ60KgRS0npvh09wjUpFVqjOQ8TMVa0bSvnaTdEzJETDl8Q2rVL/7rNgJ7eywH5uWA050ciTJX5/bBjh351egvK4yJ6jytKz9mnT+tbt9nQ8r7iDkvz833TGaHTSrfNZkNcXBw++ugjJCUlYcaMGfj222+RlJSEjz76CHFxcbBpGDBy48aNeOWVV7B9+3bEx8cjNzcXMTExSE9PL/jOlClTMHXqVHz55ZfYuXMnoqOj0adPH9ygg2GGgE/s9Va6eaxugCbTxSFqMUSp5FCs7sLo4cQqMFCO8Wu0IVLEWOHvLx99oR1W2YRZT2sDTk4OSy9c0L9uM6C3Myo+UebjklEQoXS3acNSxdSTcBI+rgcHy2fj9cJika0h9u3Tt26zoaV5efXqcr4Yw2mvx2mlG2CK95gxY7Bv3z6kp6cjIyMD+/btw5gxY4qN360mf/zxB4YNG4bmzZujVatWmDVrFs6cOYPdt4LuSZKEadOm4Z133sGgQYPQokUL/PTTT8jIyKD44QZBlHJXtaqcJxPz2xG1o8W9RQO0GFIUvRQe3uZGU7pFjRW0wyojKk43ANx1F0uXLdO/bjOgt7LZsydLS/GlKwQRnrCNei3MgKhxncM3YObMEVO/WdBy7A8MlM91b9umfvlGxyWlG2DK7a5du7BkyRIsXboUe/fuhSTARuD6LZvYyFv2VQkJCUhOTkZMTEzBdwIDA3H33XeX6gCO0A/u+VRvBxpKyKzodkQ9CJWxumlnsTB67fTy8o2mdIsaK3hYQQovKC7aBCA7q/JF80Nn0HtBpFYtliYk6FOfs/BxQk/nrNWqsZSc/LmO6DlggwYsNaLzUKOQny8vKGl1X/myRZlLBh7r16/Hc889h9OnTxco2haLBXXr1sWPP/6Iu/jytMZIkoRRo0bhzjvvRItbB3aTbwV9i4qKKvTdqKgonC5F08rOzkZ2dnbBax4WzeFwwGFQm1cul1HlK4mUFCsAP1SokAeHQ9+Anw89ZMXSpX5Ytiwfjz1mMK9RHuJpf7h2zQLAH+XL58Ph0PfaVKzoj2vXLNi4MRcNG9IMm5OWxu6V4OBcOBzOXxdX+0L58v4ALLh2zbV6tObiRT8AVkRE6DtWPPmkH77+2oqUFP3HKC1wd2yQJODKFdY3wsIcuh//6NQJAGw4dw7IyHBAw5NrpuTyZdY24eHO37eePCfCw9kz4tgxIDvbAT+Xt2u0ITWVjRPly+t3vzKF0YZr14C0NAeCg3WpVnVEzCOTk1k/iozUf64BAMOGWbB4sT927TLf/FlLlH2BqUBswA0K0mbsHzLED59/bsUvv0gYM8Y7vJY625+cVrpPnDiB++67D506dcLnn3+OJk2aQJIkHDlyBF988QViY2Nx4MAB1KtXz22hnWXEiBE4cOAAtmzZcttnliIuLCVJuu09JZMmTcK4ceNuez8uLg4hISGeC6sh8fHxokVwiZMnOwGIxpkzB2C36+vVLCWlHYAaOHjwOuz2TbrWrRfu9ofNm2sDaI2cnBTY7X+pK1QZXL7cF0AQ4uISUK3aEV3rNjLnzt0NoAKOHdsJu931OD3O9gVJuhNARaxbtwdZWcaJ4bFv3x0A6uLq1X9gtx8v8/tqceVKYwBN8McfV9Cli/fYvrk6NjgcfsjJGQAA2LEjDocP6zsxys21ALgfADB//jpUrpyla/1GJzm5H4AAHD68CWlprpmpuPOcyMy0ArgPALB4cTxCQ42hsBw+3BJAPSQnn4DdfkyXOlmkjwcAAHPmbEbNmgYzE3IRPeeRGzfWAtAGeXn6zzUA4OrVIAB9AQBLl65GcLB3bcB4Snx8PK5cYdfIzy8f69bZNYkKcOECe84mJOTAbv9D/QoEkOGkS3ynle5p06ahc+fOWLt2baH3mzRpggcffBC9e/fG559/junTp7smqYu8+uqrWL58OTZt2oQaioCe0dHRANiOd1XFId6UlJTbdr+VvP322xg1alTB67S0NNSsWRMxMTEIE+FBxgkcDgfi4+PRp08fTZ3Xqc1nn1kBAF27tkRsbAtd675wwYLNm4Fr1yogNjZW17q1xtP+cPgw27Zo2rSK7tdm0CArfvwRSE+vj9jYOrrWbWRGj2ZD8z33dMCdd7q20+1KX/jhByuOHgVq1WqH2Fjj7OzOncvGis6dGyI2tr5u9R496odFi4Dr1yt7xTjh7tigNL8cNCgGVqsGwpVBaKiEGzcsKFeuF2JjjWOFIZrcXCA9nY0PDz7YHVWqOPc7T58TVquEvDwLKlSIQd++xmiPJUtYx2zTpgFiY7Xf8OFER0tITrYgIOAu0/ZNEfNIkXMNgFnwPPssy2dn34uHHjJn26mNsi+cPMn6QliYBf37a9NGAQEWLFoE3LgR6BXPWUC2ki4Lp5XuDRs2YNKkScV+ZrFYMHLkSLz99tvOFucykiTh1VdfxbJly7BhwwbUrVu30Od169ZFdHQ04uPj0eaWe8mcnBxs3LgRkydPLrHcwMBABPJT/QpsNpvhFVozyKiEn1ONiPDX3VywQweWXrliMdU1cwV3+wP3wlqhgh9sNn3tBuvf0qfWr9e/biPDHctFRbl3rzjbF/iEPTXVCptNgGZVAvz/r1JFX7m4l2ZJ8q5xwtWxIevWxnJICBAUJOY6BAczXwO7d/vj8ceFiGBIUlPls+5RUTaXvUC7+5wICmLPirQ0/Z/fJcE3lypU0HecKFeOpTt3+uPFF3WrVhP0nEdy7/eVK4t73leqxBYVN270x9ChQkQwLDabDVlZrC+Ehmr3DOzSRc5nZ9uEhKVUG2evldO9/syZM2jZsmWJn7do0aLUs9Oe8sorr2DevHmYP38+QkNDkZycjOTkZGRmZgKQFf+JEydi2bJlOHToEIYNG4aQkBAMGTJEM7kI5xEZ95XHhAaAxET96zcyIj2K8rBhxax7+SySpE/IMEB2kmU0hyYiQgEBAF/L9XVHOyLHak6rViylyAaF4WNDWJi+YZf4hpSR7o1b/nR1D2t3xx0sNZoDSqMjMiICh3ufp7YrHj3uKaUjPV+LFOK00n3z5s1SzziHhIQ4bdPuDt988w2uX7+OHj16oGrVqgV/ixYtKvjOmDFjMHLkSAwfPhzt27fH+fPnERcXh1BR8QmIQohU7pQDyM6d+tdvZES2C99ZZDso+tdvRNLT5Z0srSeTXKk10kQaEKd08/rS031b2RMd2gcA+vRh6cKF4mQwIrxt9FY0+b1hpDBuosYJ3jcV00/CCUS1l5LevVm6ZAlFRygOPdrIYgH46eBN3uliqURcWic9cuRIgZfwolzWeNbmTFgyi8WCsWPHYuzYsZrKQriOJIkPFxEQAOTkAEnG8RdlCEROsJUD+5EjQOfO+stgNHh7+Pkx814tMepOt147/UVRhkg5fBjo3l3f+o2C6LEakMNUEYURZYXAPZbn5Ohbb2mICmung79gr0RkGELOnXfK+dRU/Z8xRkevNuKLhr4WntOlQxW9evVC69atb/tr06YNevPlI4IohowMgEdmE2Va9PTTLP3tNzH1GxWRpqR+fkDr1ixfxEejz6JsDy08hyrh96KRdrrz8uSzmnovBFksQPv2LL9mjb51GwkjmIFyPxyZmbI/EELcImlMDEu3btW33tIQtXPKn1mAb1vEuIoRdrqbNUOBY0iaC96OXmP/o4+y9Msvta3HaDi9052QkKClHISXw8+J+PnJTkj0hsfTPHVKTP1GhU8aIiPF1M/DG/79t5j6jYaek2oj7nRzx36AGOuL3FvRsfJ8OJqMEXaklL5SDx4s7HzHlxGldDdrJufz8yE8VndGBluQAfRfHFIGxPFlixhX4eOKyMU8gFk9ZmYC8fHAM8+IlcVo6LUwwv35+BpOD5u1a9cu8+8atwkkiCIoJwpa796VBDcrKuGEhM8iWunm5+NOnBBTv9HwdaWb//9WK/OYrDcPsBC8htr91xvRYwLAnhO3IoHSjpQCUUp37dpy/tAhfesuDj5m+fuLWZzjkTd27NC/bjOSny8fGxK5mAcAzz3H0j//FCuHEdFrwVW5UHX2rLZ1GQmP1yqvX7+Or7/+Gm3btkU77hWJIIpgBMc8HTuyNDubzBWVcAdmos5vtrgVst1IZosi4ZNJPRQefq7qxg3jOJURvUDHd2GWLtW/bqMgekzg8IAp8fFi5TASokx0AwLkvBEWrpXXQcQ4UbkyS7ds0b9uM5KayhRvQLzS3a8fS8+ckS2bCIZe5uVK/ym+5NzYbaV73bp1ePLJJ1G1alVMnz4dsbGx2OVrvt8Jp+FKrkilu1o1OU+TOBnRCyI8NBAgP5R9Gb7LqIeDF97meXlybGbRiAoDxOHXxJfD2IkeEzjdurFUtCmzkRB5Lvaee1hqBCsQPcfJ4mjalKVGcixnZHifCQ0tvIAjAqXDVl9S+JxBz/GFK/YaRps2HC49ys6dO4cJEyagXr16ePzxxxEREQGHw4GlS5diwoQJaNOmjVZyEibHCJO4gAA5vuaqVeLkMBIOh+zgTlRMXr6bBfjW4FsSeoYEUra5UeKWinbi1bUrS8+f990JtRHGa0BW8nbvNo4lhmhEnovlE/GVK/WvuyiiQqdx7r2XpXa7mPrNhhGcqHEiI2U55s4VK4vR0NOfB3fO+MUX2tdlFJxWumNjY9GsWTMcOXIE06dPx4ULFzB9+nQtZSO8CH6WR7S5YpUqLE1JESuHUVAqWqIm2ModxePHxchgJPS0ClE6NjSa0i1qclanjpw/eVKMDKIxitLdvLmcJ5cxDJH3B3d6aYSxQrT1XIMGcp4WhMqGhyFUmhWLhId927tXrBxGQ89F74YNWXr+vPZ1GQWnle64uDg8//zzGDduHPr37w8r97lPEE4geveKw50k/e9/YuUwCnzyFBAg1uTrrrtYGhcnTgajoLfCw+sxwkQaEO8522aTPWcbycGcnhhF6Y6MlJ3p/fqrWFmMgkilu39/lv7xh/51F0V0H23SRM5TRJSyEW2ZUJQRI1i6fTst6HFycuR20mN8eeQRljocwLlz2tdnBJxWujdv3owbN26gffv26NSpE7788ktc8rWo5oTbGMUxT48ecl4ZmshX4Q8bkV6KlRw7JloC8eg9meRnIo0Sb1b0TjcgW8T4ahg70edllfDFwG3bxMphFESal/PdXb7jLRLRSndIiJw/cECMDGZCdHsVhS8gAQC5o2LwZ6/Foo9FAveLAPhOGzitdHfp0gUzZ85EUlISXnrpJSxcuBDVq1dHfn4+4uPjccMo2ySEITHKgKu8yZcvFyeHUTBCaCAAuPtulvqSmVFJ8AUqvXYE+OTdKLu6RrCKsdlYun+/OBlEYoQ24DzxBEsTEsTKYRREjtlK/xtJSfrXr0TvcbI4uBPQPXvEyWAWjDIH5FSsCNSqxfK//y5WFqOgjJyihzGzn598D+3erX19RsBln6AhISF49tlnsWXLFhw8eBBvvvkmPv74Y1SpUgX333+/FjISXoBRBlyrFahaleXnzRMrixEwwsQFANq3Z+mBA+TBXM+QYYDxYnWLNi8H5PN+a9aIk0EU+fmyYmcEp0d8UrZ+PZ2dlSSxY7ayP4hWNPUeJ4uDK20LFoiTwSyIPoNfHDyMrC858iqNq1dZ7D09x31fu4c8CsTRuHFjTJkyBefOncMCX7lihFtwpVuUh2wlfFeVTkcYZzGEK90AcPiwODmMADf518u0lz9gjRAGCND//y+Oxo1Zyne8fYkbN+SFLyOYl3N/D4D43VXRZGfLcYVFm1UnJoqpn2MEa4wOHVhKR9XKRnQoyOJ4/HE5b5TjVSIRseDN536+YiytSvRLq9WKgQMHYjnZ6xIlYBTlDgCef56lO3cCmZliZRGNUdqlWjUgOJjlt24VK4to9HY4YzTzciM43OGhqvbv9z3LC379bTbZiZlIlEeCVqwQJ4cR4LuFgLgF7AEDWCra+ZQRfD9wpS05WQ69SRSPESyYivLgg3J+xgxxchgFvvCg50LWkCEsTUlh95G3o4rSTRBlYaRwEXx1GqBdVaMo3UoZfP3spt5tYjTzciP0yWbN5LxRroteGMkqicN3V33l3F9J8LYJCdHnzGVx1K7NUtHh9IygxCnDC1K4y9IxgmVCUSwWeXHXF48SFeXKFf3Ny5X30JYt+tUrClK6CV0wwqo0JywMaNSI5X39XLcRFBzO4MEsnTVLrByiIaWbpSL7ZFiYrHT6ildVjhGuf1FGjWLpTz+JlUM0RmgbvnAuWkkxgt8Bf385vODPP4uTwwwYaQ6oZMoUlq5dK1YOIyCijfz9ZcuyH3/Ur15RkNJN6AI/zyM6ZBiHDyq+/qA0Umigtm1ZmpLiuw6TJEl/hzN858EoZ7qN4nCHHz3xNQ/mRlDsitKlC0tzcnzP3F+JEcZr7vhIdFxdo8wpeFv89ZdYOYyOESwTiqNPHzl/9qw4OYwA3+nW2xqhZk2WxsXpW68ISOkmdMFoE7kXXmDp5cvyw9sXMcJuAUf58PPVuKcZGbJS4Ys73ZJknLGid2+W/vOPWDn0hnvHFn39lXTtKud9wQSxJETG6OZwj8+AOAdiIhYnS2LgQJYaZdHSqBjRvByQLRUA4PvvxclhBERZI3DfCHl53u+U0N+ZL7niII3ChhFFMdIDkvP448Czz7L8r78CzzwjVh5RGMFpFad6dTm/caMcKsiX4A+9gACgXDl96jSS0n39OnvwAuIXglq0AFavBlauFCuH3hjRDLRCBRbTNT8fmD+/sEdzX8IIbdOggZz/5x+gdWv9ZcjM1H9xsiQ6d2bp4cPMs7y/U7Nq3yI/X3a8Z6RxBWDnunv2ZCEJP/wQGDdOtETiEDW+KDdcVqwAHntM3/r1xKnhYSBfyruFxWKBpLD/tFgsBfk8PmMiiFtkZMjmwqIfkJygIHkSt3YtKd1GcZpUpw4LRWMEBVAESvNRxbCqKXznITVV/KSR7xaVKwcEBoqTA5DDhomWQ2+MuiPVpQvw55/iQ1WJxAhKt3Jc+vtvMUo3f25ZLPotTpaE8v/fvx9o106YKIYlNVVeJDGa0g0wy8f161n+xg3jzFP15to1dnNHRupbr5/C5jouzruVbqfMy/Pz8wv+4uLi0Lp1a6xatQqpqam4fv067HY72rZtiz/++ENreQkTonxAci+0RuDTT1n6889AVpZYWURhFFNezsMPs3TSJLFyiEJEeygVfNGxSo2k8HXrxtIzZ9hZYl/BqGcvR4xg6erVcjQMX8MI5uWAfPTCbhdTv3KxWK/FyZKoXFluD1/3EVMSvN+GhbFQhEZDqeTNnClODtGItEidOJGls2Z59/PW5TPdI0eOxH//+1/07dsXYWFhCA0NRd++fTF16lS89tprWshImBxuVlShgvgHpBIebxQADh0SJ4dIjKZ085i8DodvOkwS8dCzWmWPxKItDIywk8dp2FDOnzkjTg694T4ujBDeUUmPHnLeV0OHGeX+CAhgqaiwZco5hRGoWpWle/eKlcOoGKXfloTFAgQHs7wvR0gQqXQrY6bv26d//XrhstJ98uRJhBfjLjI8PByJvmz3RZSIUQfcBg3kmKOffCJWFlEYzWlSv35yfsUKcXKIQtQiCL83RTsDMtIuq80mT6a3bhUri54YbSGOEx0NtGnD8r4a6tEoz1LuukfUIp1RrgPn5ZdZumGDUDEMi5EsmEpi6lSWHjggPga9CET7XmrSBKhWjeXffFP/+vXCZaW7Q4cOGDlyJJKSkgreS05OxptvvomOSreWBHELrtiJDu1RHNxZ1y+/sN1VX0KS5B0Dvc/wlARXcgBg7lxxcohClMLD701+r4rCaJMzbm3hSztYRlW6AdnZ4uzZQsUQhlGepVzZdcHHrqoYaXEOAPr2lfO+ZBXjLEZrr+LgR9sANh/0NbKzrcjPZ6aoosZ+vumyZYv3RhVyWen+8ccfkZKSgtq1a6NBgwZo0KABatWqhaSkJPzwww9ayEiYHCNP4pQnItauFSeHCG7eZI6zAOMo3QDw3HMsPXZMrBwi4JNqvb3J83uT36uiMNoO1p13svTCBbFy6InRIk0oUTq8TE4WJ4cojPIsrVyZpZIkO0nVE6MtzinDTs2aJU4Oo2K0cb04KlWSLWlELSaJJCODeVD18xPne+mtt+S8ty58uKx0N2jQAAcOHMDvv/+O1157Da+++ipWrlyJgwcPooEyloRAvv76a9StWxdBQUFo164dNm/eLFokn8ZoHrKV9Owp54cPFyeHCLiCZ7XK55mMwEMPsfTwYd+zPhA1OeH3pmil2yiOojgdOrDUWycAxWEUxa44lH44fNFvq1GepUoP3SKOpBhNifPzk53LTZ8uVhYjYrRFkpLgc8Dt28U/C/Xmxg3mqCEysrA3cT1p3JgdIwKAF18EsrPFyKElbl1ai8WCmJgYvPjii3jttdfQp0+fQmHDRLJo0SKMHDkS77zzDvbu3Yvu3bujX79+OEM2P8Iw8iTOzw945RWWT0gQs2ovCmW7GOT2BQB06iTnvdmhRnGImkzSTnfxdO8u5zMyxMmhJ9wzuN7WFs5gs7FwjwA7e+lrGOVZqgzTdfCg/vUb0VyZLwj5qmf90jBiexVHbKyc97W9Oq50i24jZZx0UdERtMRlpTs/Px/jx49H9erVUb58eSQkJAAA3nvvPUOYl0+dOhXPPfccnn/+eTRt2hTTpk1DzZo18c0334gWzWcxykShJJRO1F56SZwcemPUdomMBGrWZPmxY4WKojuiJiekdBdPly5y/vBhcXLoidF3pbgJ4uef+16EA6OM2RaLbIor4liWEfsot9DKywM2bRIri9Ew2rheEtWqyX5+xowRK4vepKUZQ+l+4QXZZ8WgQd5nZeay0j1hwgTMnj0bU6ZMQQCPGwGgZcuW+P7771UVzlVycnKwe/duxMTEFHo/JiYGW33J/azBOHqUpRERYuUoieBgoEYNlp85E1i/Xqw8esFjMouewBVHs2Ystdt9y5OoqMkkvzePHNG33qIYzbxcaQGyZo04OfTC4ZAd2BilDYpy331yfulScXLoTXY2kJnJ8kZ6lv79t/51GnHnlHteBlhMedELmEZixw6WGnVMUdK5M0sPH/Ytp3gpKewgt+g2sliACRPk1zNnAt995z2RAfxd/cGcOXMwY8YM9OrVCy/zOAkA7rjjDhwT7Pno8uXLyMvLQ1RUVKH3o6KikFyC15Xs7GxkKw4OpN066OpwOOAw6IFSLpdR5SvKrl3+ACwICsqDw2HMrYlffrGga1d2O9xzD3D8uKOQcxQj425/2LLFD4AVAQESHI5cDSRzn7fesmD1atYeAwdK2LPHWPJpxeXL7F4JD8+Fw+H6WQd3+0JICOsLu3aJ7QtXrvD/32GY8/yxsVbY7X74z3+A0aMNIpSTuNofmJd2GwCgXDnjtIGSNm2AyEh/XL1qwaOPAocPOwrFVPdWdu+2gE/ZQkJcbxu15w39+/th714rli/Xf8zwdJzUilGj/DB1qhUHDwITJuRhwgRjzncAfeeR586xMaV8eWO1V3G8/DLw3XdM3scey8fGjXmCJdIeh8OBI0fYCla5cvlwOMT+zy+9BGRl+eGtt6xYs4YteA8dmo9u3YzbFs7eRy4r3efPny/WYVp+fr5hlMCi58slSSrxzPmkSZMwTnmI4BZxcXEIEeXCz0ni4+NFi+AUDkcPAOFIS9sHu/2caHGKJT8fePDBZli2jM3eXn45Ca+/bq44Qa72h+PHmwBojIyMa7DbjXWAKS/Pgo4dO2DHjqo4dMiCWbM2ICrK+w/VXrgQAyAYR49ugcPhfswMV/vCjRvVAHRAdvZN2O3r3K7XEyQJuHTpPgBW7N+/HklJmULkKErbtlGw29n2x6+/rkZQkHEf/CXhbH/Yu7cygK4AgNWrjXugbujQ6vj88/YAgA8/PInHHz8uWCLt+euvaADM4cWqVe63jVrzhuDgSADdkZNjgV3nw5fnz7Nx8vjxP5GXl6pr3aXRqFEIgD4AgClTrGjd+g+EhBh7wVjreWRengUAC+x+6dIa2O3G9o4lSUCHDh2xc2dVbNvmh7lz41GxYpZosTTHz495Db1xIwF2+yHB0gCVKwegV69muHmTWVTbbJdgtycIlqpkMpx0+uKy0t28eXNs3rwZtWvXLvT+4sWL0YYf8hFEpUqVYLVab9vVTklJuW33m/P2229j1KhRBa/T0tJQs2ZNxMTEIMyInmTAVlTi4+PRp08f2Gw20eKUyciRrJs9+GArdO58h2BpSua++4C+ffOxfr0f1q+vhVWrqgrz4ugK7vaHP/5g/9zDD4cjVulBxCD06gVUqMDyx4/fg2eeMe6ugRpIEnDzJrtXHnigG4oMsU7hbl+oVQv49FMgO7u8sL5w8ybgcFgBAI880rOQsyaRxMTI5m55efciNtbYOzVKXO0PmZlscbpr13xDjgmc2FhgwwYJe/dakJjYCLGx9UWLpDkXL7K26dfPvbZRe95w113AO++wfKNGsdAzeE16OhsnBwzoajiLtKioXDz4IJMvI6MvHn7YmOOFXvNIpWO5hx7qhcBAzapSjc6dgapVWf7Mmd546invnns4HA688w5TGocMqY3Y2FqCJWIMGaJ8VQlAU0GSlA23ki4Ll5XuDz74AE899RTOnz+P/Px8/Prrrzh+/DjmzJmD33//3WVB1SQgIADt2rVDfHw8HnzwwYL34+Pj8cADDxT7m8DAQAQWMwrYbDbDK7RmkBGQ475GRPjD6OL+8ANQrx7Lf/qprWBSYQZc7Q/p6SyNiLDCZrNqJJX7hIcz5zRLlwJTp1oxYYLVUKHN1CY9XQ6RER1t8+hecbUv8DAdV69aYLXahCw28WdWYCAQHm4zjEd9mw2oX5/5Fpg717/IRMAcONsfsm5t6ISF+cFmM/aK48iRwNChwF9/+eH0aT9dlT4RcOWlUiXP2kateYPyXPnu3TY01Wk+nJEh91NPx0ktGDCA+Um5cQN4/nl/PPussaKDFEXreSRvK5sNKF/eYI1VAtHRLJzs+vXAuHFWjB1rvPmR2nBHalFRxp+nGxFn7yGXR+4BAwZg0aJFsNvtsFgseP/993H06FGsWLECffr0cVlQtRk1ahS+//57/Pjjjzh69CjeeOMNnDlzptD5c0JfjOJx1Rnq1mVxqwG28+fNGCXma2m8+qqc9/YQHteusdTfH7rv8nKHRPn54kLeKD3cGm2S2rYtS0twDeI1mGmsVvpL/d//xMmhF0b02M37yaVL+tXJr4PNZsx+arUCkyfLr0XEMTcSZhpTlCjnHk5uYpoao4QM83ZcUrpzc3Mxbtw4NGvWDBs3bsTNmzeRkZGBLVu23OYxXBSDBw/GtGnT8OGHH6J169bYtGkT7Hb7bebwhD44HPLunVkGXe69PDWVeU30VszwMLz7bhSYWfft691x1EXGTQ8IkPsBn9TqjdE8lyvhoQT37ZMtRLwRMyzEcaKjgcGDWX70aLGy6IERPXY/+SRL9RwzlNfBaItznP/7PzmvMLr0Scw0pihRttuXX4qTQw8kSVa6jfj89SZcUrr9/f3xySefIC/P2I5khg8fjsTERGRnZ2P37t246667RIvks/DdO4tFjr1ndDp2lPPF+NjzGsygdAPAG2/IeR5+zhsR3R58Mi9qZ8bIsVx5GBlAfFg1LeHjtZFCUpVG//5y/rr7fgdNAf//jPQcrVyZpXv26FenkccJJX37svTPP+XwnL6I2cYUJVzm+fPFyqE1aWlAXh5TB41+X5kdl83Le/fujQ3eEjCN0Bz+gAwPl822jU5gIMBDziclea+iJ1rJc5bXXpPzL7wgTg6tEd0e/GEraqfbyJPpcuWYszkAWLxYrCxaYmRrg+IYNEjOT5okTg49ED0+FAeX5fBh/eo0Sx+dMUPOjx8vTg7RmKW9ioMfEzh82LvjrvNnb0iIhKAgsbJ4Oy4r3f369cPbb7+N0aNHY8GCBVi+fHmhP4JQws/CGGl13hkefljOf/aZODm0hA+0kZFi5SgLiwV45RWW37oVOHZMrDxaIdoMj0+KRO10G31yVqcOS5csESqGphh54aM4ypUDWrZkeeU5Wm/EiEp3ixYsPXNGvzrN0kdr1QKaNGH5adPELWaKxiztVRyPPCLn164VJ4fWXLnCzmkY9dnrTbisdP/f//0fLl68iKlTp+KJJ57AwIEDC/4e9PXDK8RtGHGi4Azh4ew8MQCsWCFWFi2QJNnsy+hKNwC8/bac/+orcXJoCV+gEhWpkHa6S2fgQJZ68+TZTGMC56OP5Pz58+Lk0BoeBcRIz9LWreW8XibURh8nlCjPAv/yizg5RGKm9ipKhQooiJjy669CRdEUs2zAeAMuK935+fkl/hn9rDehP2ZVugHgk09YmpIiO1fzFrKzmZM7QJyS5wrVq8tOk778ku14exuivRPzeknpLh5u/ZKWxo6deCOiF37cQRmyeuxYYWJoDn+WGqlteKhBgD0n9cDoFjFK7rkHqFGD5YcPZ+HOfA0ztVdxjBzJ0rlzgdxcoaJohvzs9WJPtQbBo0CcWTwAH6ErO3dacPZsqCk8OZtZ6Vau4nubF3Pl+SSzeBUdNUrOf/yx93kyF+2dmNd74YKY+o0+OatWTc57q6mhEXdTy8JqBRo2ZHlvNv3nofyMdlSrfn2W6rVYx69DhQr61OcJFkthXwObNomTRRRGX0wtC77YDwDHj4uTQ0u4eblZ28hMuKx05+XlYfz48ahevTrKly+PU6dOAQDee+89/PDDD6oLSNxOz55WvPrqPZg716DxMhSYWem22WTTxUWL9FvJ1wPeLiEh5nFw17Gj7Ml8xQrvWwgRPTnh9S5aJKZ+0f9/WVit8q7qW2+JlUUrzDpe//QTS1NTgT/+ECqKJuTmysqm0e4PLs+aNfrUZ8Qd/9J48kk57GW/fmJlEYHRx/WyaNVK9tI/YYJYWbSCdrr1w2Wl+6OPPsLs2bMxZcoUBAQEFLzfsmVLfM9dPhOakpPDlO3nn/fHuXOChSkDM4eLAIBHH5Xzy5aJk0NtzDq5fv55Of9//yfO6ZcWcNNeUTs4VauytEoVMfWbYXLWqRNLk5O909TQrONCu3Zy3hunIcrz0kZ7lmZmsvTW/ovmmLGPKuPIJySIk0MERrdgcgbuEC8uTqwcWsHHFzrTrT0uK91z5szBjBkz8MQTT8Cq2CK74447cMxb3QobjGnT5LPzY8YIFMQJzDCRLo0GDYC2bVn+ww/FyqImoj1lu0uzZsDMmfLrjz8WJ4vaiJ5MtmrFUorTXTLPPCPnV60SJ4cWOBwAPzFmtnEhIEC2glm6VKwsWsDvjYgIwN9frCxFufdeluoVNkz0OOkOPPoGAHzwgTg5RGCGcb0s/vUvll69Ki8yeRNkXq4fLivd58+fR4MGDW57Pz8/Hw7umYnQlMcfz0d0NDt8t2ABsHq1YIFKwRtWOUeMYOmFC8DJk2JlUQszPwgffVRWCj77DPjnH7HyqIXoySTvCxkZ+k8s8vPl88RGNhutWVPOc5Nmb4Hvdlgs5jgvWxTlgsjOneLk0AIjj9dNm+pbnxn9DlgswP33s/zcud7riLE4jNx3nSUmRs57m1NdQHlkg8zLtcZlpbt58+bYvHnzbe8vXrwYbdq0UUUoonQiIoD//GdHwWtlyBSj4Q0D7qBBcn72bGFiqIpoT9meEBZWOHzHjz+Kk0VNRFsfhIfL5/v19mDOJ9KA8SfT3LGO0Y/2uApfII2IMI+fByXNm8v5r78WJ4cWiHayWBpc6dbLQub6dZYaeXGuOP79bzm/cKE4OfREuYBrxrkGx2aT8950zJAjeu7hS7isdH/wwQcYMWIEJk+ejPz8fPz666944YUXMHHiRLz//vtayEgUQ61aN/Dhh8zMfPNm45oseYPSHR4uhwuaMME7vGYbeRLnDL17s3AsADMx37VLrDxqIHqn22IRF6ubK91WKxAUpG/drvL00yz96y/vcq5o5oU4APDzk49bzZ4N5OQIFUdVjNw2fMw4fVoOQ6kVkmTeOUXnzvIRnlGjAF+IsMvbyt/f+IupZcHHlu+/9z5/HjdvMvNys7eRGXBZ6R4wYAAWLVoEu90Oi8WC999/H0ePHsWKFSvQp08fLWQkSuDFF/ML8h9+CKSnCxSmBMzuSI3z4oty/tIlcXKoBfeEa9Z2sVgKLzTNmCFOFrUwQkggUbG6lQsOFoMHZejcWc57k6mhWZUZJU89JecPHRInh9oYuW24Z25A++NXGRmy3wEjXovSKPrMOnFCnCx6oey3Rh/Xy0IZOkwvp4F6YcYjG2bFrTjdffv2xcaNG3Hz5k1kZGRgy5YtiFEeeiB0ITKy8A7fAw+Ik6UkvOVmVq4njRwpTAzV8IZ2uesuYPhwlp85E0hMFCqOR+Tny2dqRe5m8Yms3s7UzGTeFhkJ9OrF8kqTUbNjZMXOWVq0kOOpKxdKzY6R2yYgAKhVi+X//FPbuvh1sNnMMVYU5cEH5Wfu//2fWFn0wBt8+nDatpXvv4kTxcqiNvz5W66cF5hxGhyXle569erhSjHbIKmpqahXr54qQhHO06aNfJZt7Vpm8mgkRJvMqknHjixdsECsHGrgLe2inFhPny5ODk+5fl02NxQ5sRZlXm62kCV8tzsx0XvMRPlZWZGWFmrAF0R276a20Qu++8yfK1qhNLM3685pz54sXb9evm7eipEXi9yhYUOWzpsnVg41kSTZItUsz18z47LSnZiYiLxinmTZ2dk4f/68KkIRzuPnV9jE8fHHxclSFKVHYrMrdwDwySdy/uBBcXKogbco3a1aySFrpk7V/kyhVnClMyQECAwUJ4co83KzTc6ee07Oe0vsVm8ZE957T86vXStODjUxets88ghLtR43zO6LBADGj5fzixeLk0MPzDaul8Xbb7M0Lw84c0asLGqRlgbk5lLIML1wWulevnw5li9fDgBYvXp1wevly5dj2bJlGD9+POrUqaOVnEQpVK4MTJrE8gkJwOjRYuXhKM+Ym9EUrCh33innzX6G2EzmvGUxZYqcN2uM3rQ0loreyRJlXm62yXTdunL+00/FyaEmRlfsnIXvRgFsIc4bMHrb8MW6W1NEzfAGJa5lSznv7SbmfEHSG8zLAaBfPznvLbvd/NkbGJhreCem3oDTSvfAgQMxcOBAWCwWDB06tOD1wIED8dhjjyE+Ph6fffaZlrISpTBqlGwa8tlnxghnw03i/P2B4GCxsqiBnx/Qvj3Lmz0OrFnDrhSHchJj1tjJRplUizIvN7J35pJ44gmWHjsmVg61MEofVAPu9OjIEbFyqIXR24aHVPJzy0uQ85hxnCiKxSJbzaWn67/AqSfc8sxbjnnYbLLjwE2bxMqiFvyeCgvzonAPBsbpITI/Px/5+fmoVasWUlJSCl7n5+cjOzsbx48fx3333aelrEQpBASwM2ycmjWBpCRx8gDe5bmSw6Pi/fUXcPGiWFk8wRsmL0q++oqlf/yhvQddLTDKpJr3B71X8c24g8WdqF244B2eso3SB9WAt83Zs8DRo2JlUQOjtw0/p7xvn7YhNc1mEVMSr78u57050i4/K9y3r1g51GTsWJauXi1vXpgZ/uwNDSWlWw9cXpdMSEhAJW+ZqXsZdeoUPmv43XfCRAFgzol0WShNzO12cXJ4ire1jdJz/5Il4uRwF6NMqrnnZ737hRkn040by/lly8TJoRZ8giz6iIMaNGsm53/7TZgYqmH0tuHOXAFtrWS85bml3DH96SdtFypEwtsrOlqsHGqiDNS0bZs4OdSCP3tJ6dYHf2e+9MUXXzhd4Guvvea2MITnfP892+nbsAEYN445mVLGldUTbwoXwYmIAHr3BtasYQsczzwjWiLXyc2VJ3Fmn7xwqldnznwWL2a7XGPGmMu6wihK9x13sPTaNeYIUWtzUY4ZLS9sNuDZZ4Eff2S7VUoHXmbEjG1QEgEBwNNPA3PmsHbhDpDMitHbJjycjV03bjBZtZLT6NfBFWbPZhYCGRlsPqEMS+otmHExtSyqVQPatWOWpXyObWbIvFxfnFK6P//8c6cKs1gspHQbgHfeYUo3AAwdCuzfDyEOErxlVbooQ4awh6QkMQdYZjsXzRVuwLtCRLzwguwN9vRpZvlhFrj38ogIsXLwezU/H0hN1a9/mHWs6NePKd0Ai9RgZseEZm2DkujblyndeXns7Gy5cqIlcg+HQzZjNXLbVK7MlO7ExMJWIGriTUpc165yftIk71O68/K8b3Gf07YtU7q3b2fzQDMt8BeFzMv1xal9jISEBKf+Tp06pbW8hBP07g289BLL//23ONNHb5vEcZRh2fiE20xwBS88nDm58xZ695bzM2eKk8MdjHKvBATIu+16OlMz62Tam7zZetMuIgAMGCDnFy4UJ4en8PEaEL8oVxoBASzdtUu7OowyTqpBQADwxhssv349kJkpVh61SU2Vzea9ob2UKL3Om93EnMzL9UUn40FCb959V84PGSKbr+qJN5qXA8xqoEkTlv/gA7GyuAMPT2W2HfqysFiAHj1YfuJEoaK4jJEUHhFhw4z0/7tCuXKyRYUZxwJOTo48LnjLBDk0FKhRg+W//VasLJ7A742ICGMvklavztKsLO3qMOs4URL/+pecX71anBxawJ8fYWGyd3tvoXVrOf/f/woTQxVop1tfnFK6R40ahfRbQZdHjRpV6h9hDGrUACZPll+vWqW/DNy0yMir8+7y4ossTUszXzgMo5wf1gLlCnRqqjAxXMZIO716hw3LymLmv8q6zQQfC1JSzOsQie+mWixAhQpCRVGVhx9mqd4h8NTELLu73bqxVMtrbaRxUg2iouT877+Lk0MLzNJv3cFikS3rDh4UK4unkNKtL04p3Xv37oXjVsC9vXv3lvi3b98+TYRMTEzEc889h7p16yI4OBj169fHBx98gJycwp3kzJkzGDBgAMqVK4dKlSrhtddeu+07vsSbb8oK7+DB+k88vFm5Gz5czv/yizg53MGb2+WRR+T899+Lk8NVjDRB4TLotfPC/3er1bjemUvj1VflvDJso5ngbRAZydrBW3j6aZYmJADHj4uVxV3Momhy+X76SZvys7OZ3wRlXd4At5D54QdzLw4VxVstHTk81NvRo2x8MSuyRYLv6kp64pTSvX79ely9ehWSJGH9+vUl/q1bt04TIY8dO4b8/Hx89913OHz4MD7//HN8++23+M9//lPwnby8PPTv3x/p6enYsmULFi5ciKVLl+LNN9/URCYzYLUCU6bIr//3P33r92blLjBQzs+ZI04Od/DmdrFYZIdJy5eLlcUVjKR0c/NQvXZtlf+7GR3SKJ2nxcWJk8MTzKLYuYoydJjezz+1MItJdeXKLFXu3qoJvw5+ft5ljfHQQ3I+Pl6cHGpjpGeaFrRrJ+dXrhQnh6fQTre+OH2mu2HDhrh06VLB68GDB+PixYuaCFWUe++9F7NmzUJMTAzq1auH+++/H6NHj8avv/5a8J24uDgcOXIE8+bNQ5s2bdC7d2989tlnmDlzJtL4YTUf5PnnZS+Zzz3HvBLrhTcrd4Bsvv/HH4BOt4Iq8HYxs6fl0uDBFjZvlndGjI6RJigPPshSvc50G+l/d5cnn2Tp+PFi5XAXbl5u5jYojsBA4LHHWN6sbWOW+6N9e5ZqNW4oIzzoFcpQD1q2BLp0YXnl8SizY5Z+6y4hIfKz8sMPxcriLpJESrfeOO2WQyqy7WG32zFp0iTVBXKW69evI1IRz2bbtm1o0aIFqlWrVvBe3759kZ2djd27d6Nnz57FlpOdnY3s7OyC11xBdzgcBSb1RoPL5ax8b75pwdatrKnnz8/F4MH6bGHduOEPwILg4Fw4HCY97FgKDz0E/OtfzEPI77/n4umnxfyPrvaHS5f8AFhRoUI+HA6THUh3gv79AYC1y+7dueja1fh97/p1dq+EhDjgybDjal8ojvBwCwB/XL6sT/9ITmb1RUaatz/27m3BvHn+yMoCrl1zGGZBy9n+cPUqa4OwMPO2QUn07m3BwoX+uHkTyM52mE5hS0lh43VERB4cDvdXzdUYG0qDOea04cYN4OZNRyFrMDW4coX10fBwCQ5HrrqFC+bhh/2wbZsVqanA2bMOREdrX6fW/eHiRdZvIyM967dGpkcPPyxbZsWlS8CNGw4hoXk9IT0dyMpic6WwsBzD6jxmwNlrZ2BfmCVz8uRJTJ8+HZ999lnBe8nJyYgqYtcUERGBgIAAJCcnl1jWpEmTMG7cuNvej4uLQ0hIiHpCa0C8k7ZIVisQFNQfWVn+ePFFCaGhdo0lY6Sk9AEQggMH/kRmZqoudepN1aq9kJRUHtOnX0WlSmJjRzjbH3btagGgPq5fPwm7/Yi2QgkiNLQfbtwIwPTpp5CaelS0OKXicFiQk3M/AGD79ngcPuz5g8/ZvlAcCQlVAHRBQkIa7PaNHstSFps21QHQCrm5F2G379C8Pi0ICPAH0B8A8MEHh9Cr1xmxAhWhrP6wfXtdAHfgxo0k2O0axnwSQFCQDUAsAGDq1O1o1uxq6T8wGPv3twZQG5cvH4fd/o/H5XkyNpQGs6J7AADw1Vfb0KTJNVXL37OHjUv5+Wmw2zeoWrZoqlaVx4833zyFJ544plvdWvWH/ftbAaiDy5f/ht3+tyZ1iCY8PAAAixv54Yf7cOedF8QK5CKXLgUDiIG/fx6CgvI06wu+QEZGhlPfc1rptlgssBQ5cFf0tauMHTu2WIVXyc6dO9Ge2y0BuHDhAu6991488sgjeP7558uUR5KkUuV8++23C3ldT0tLQ82aNRETE4Mwg8ZUcjgciI+PR58+fWBzMhbD/PnAoEFAZqYNN2/2x6OPar/7l5HButeAAV1Rr57m1Qnh//7PD++/D+zfXwW9esWqvrrvDK72h8WLmaekDh3qITa2jsbSiaF7dyvsdmDp0kZYsKCuaHFKRRmHd9CgPh6FV3FnbChK5coWjB8PnDpVAf36xWp+znr/frb12KxZFGJjY7WtTEP+/W8JZ85YsHRpa3z2WQvR4gBwvj8cPMjaoFGjqqZug5Lg5v/bt3fD6NHm2sn//ns2Xnft2hixsQ3dLkeNsaEsAgIk5ORYYLF0Q2ysurubmZlsIKpePdQr++hXX+Xjzz/9sHhxY3zxRT3NzbK17g+zZvF+2wixsQ1UL98ojBwpITXVgpUr22PiRHNZYOzdy9JKlSywWKDp2ODtOHuM2SXz8mHDhiHwllaRlZWFl19+GeW416JbKM9Zl8WIESPwGD9wVQJ1eBBUMIW7Z8+e6NKlC2bMmFHoe9HR0fjrr78KvXft2jU4HI7bdsCVBAYGFvxPSmw2m+E7nysy3n+/nP/0U3888YRGQt0iMxPgCz/R0Tavi9PIGTxY9mK5d68N3buLk8XZ/sBDuVWpYoXN5kWuihU89hhgv2XQkZNjQ5FhylBwx2WBgUBIiDo3iifjF49BDwDXrtk0c4wk18HSypX9YLOZzPZXwbBh7GzfhQsWwz07yuoPfKwODzd3G5RETAxzcnf8uPn+P74oFxWlznit5dymdm3gn3+AtDT1ny2ZmSwNCzNfGzrDf/7Dj0YBa9faNJ+jcbTqD/xsvzfPMwAWIeGLL4CEBOON+2Vx/TpLK1ViC1pm0HuMirPXzemRa+jQoahSpQrCw8MRHh6OJ598EtWqVSt4zf9coVKlSmjSpEmpf0G3DkmcP38ePXr0QNu2bTFr1iz4FTmY1aVLFxw6dAhJSUkF78XFxSEwMBDtlG4GfRSrFVi4kOX37QN++03b+rhzBn9/ftbLO2nUCAXnr4YOFSuLs5jFG64n8J0twPgeYY3mcFDhKkOXEDbe0h9HjpTzq1YJE8MtjNYH1YYb1B0/Lk80zYKZHFLxuOhXNbDg9/Y+GhsLNG/O8srnl1n580+WmqHfesJbb7E0K0v+n83CmjUsrVjR+H5vvAWnd7pnzZqlpRylcuHCBfTo0QO1atXCp59+WsiLevQtjScmJgbNmjXDU089hU8++QRXr17F6NGj8cILLxjWTFxv7r1Xzn/4IfDAA9qF6DF7GCBXGDgQ+PZbFqsxOxtCTMxdwVvDAylR9rk1a1gbGRW+02ukMDgNG7Idq+TkwmGXtGDfPpaavT8q2++334B+/URJ4jq8D5oxTroztG4t57duNVfbmEnp5gvQBw6oX7YRx0m1efVV4OWXWf78eaB6dbHyuIvyeKvScsobUfhuxowZQLdu4mRxldxb1vDp6WLl8CVMYaMTFxeHEydOYN26dahRowaqVq1a8MexWq1YuXIlgoKC0K1bNzz66KMYOHAgPv30U4GSG4vwcOCTT1h+715gh4Y+i3xBseOMHSvnv/pKmBhO4y07i2UxYgRLjd4mRmwPrnxt2KB9XQcPstTsk2mLBfjXv1h+wQKxsriKEfugmgQFyYo3D/VoBvLz5V1jM7QNHzeKnPRTBW/vowDwwgty3iyWc8XB28pmK6yUeiN+fsCbb7L8nDmFFxyMDp+nP/gg7XTrhSmU7mHDhkGSpGL/lNSqVQu///47MjIycOXKFUyfPr3Y89q+zJAhcl7LyYcvPCA5UVEoODP85pss9qFRyc0FUlNZ3tsXRB55RM6npIiToyyMuEBlvXUEL1djvzD5+XLM3TZttK1LD7p2ZSkLmyRWFlcwYh9Um44dWbpRe4f8qpGayr2Cm6NtWrZkaXCw+mX7Qh/185NN9NeuBc4YKwiC0/iSpSMAvPiinDeTibncTgaetHoZplC6CfWoVg14/HGWX7ZMu3rMZBKnBsrd1MOHxclRFlzhBoCICGFi6EKXLnJe4OmYMjHiAhV3Dqz1mW6lUmFWU0old94p57X2m6EmRuyDavPSS3L+4kVxcrgCb5fy5YGAALGyOAP3e3vjBpCTo27ZvtBHgcKbIV98IU4OT/C1+V+jRvLGy7x5YmVxBV9rJyNASrcP8uGHcv5//9OmDl9YlVaidML/zjvi5CgLHtUgJIQ5ufNmbDZ55/HLL8XKUhpGfPBxWbQ2kzabUlEWkZFAjRos/957YmVxBSP2QbVRWlLMnStODlfgz1GzKJoVKsiWKwkJ6pYdF8dSb+6jAFCvHnD33Sz/2WfanI/XGl+b/wEsegXATMzNYuXki+0kGlK6fZD69eX8999rU4evrEpzAgNREC7MyN6y+cPAWz3AFuW++1iqhTddtTDig49bQWjtWMsbxwl+hCcxUagYTpOTIy/GGakPqo3FIvfnTZvEyuIsZlsM8fOTLVeOHVOvXOUxl9q11SvXqHzwgZw38oJxSXjjuF4WTz0l59etEyeHK/B2iowk83K9IKXbB7FY5IH899+1mRyabbKgBjx0fGYmsHSpWFlKwtvDrhRl8GCWZmQA27eLlaUkjDhB6dSJpVovVhhxwcFTuFMdQA7JYmR4G/v5md+ZXVlMncrSFSvksdDImPE5yndp1Rw7uOdywPu9YQNAz56yEjdzJvDzz2LlcZUlS1hqpn7rKZ06sagfAIsMlJcnVp6yUPr3MdLcw9shpdtHuf9+Ob9ypfrle+Nkuiz4gAsA779vTIdqvqZ08zOGgHZHKTzFiBNr/hDOyGCLSFphxP/dUypXlvO//ipODmfhbRARITvQ81b69pXzRl2EU2I283IAqFmTpSdOqFcmvw4VKnj/sSjOa6/J+VdeMY/JMiCfb/b28aQoPHoFAOzZI04OZ7h2TZ6jRkaKlcWXIKXbR6lZE4iJYXm++q8mRty90xqrVQ7JduSItiHZ3IUr3eXLi5VDL/z8gOeeY/nFi8XKUhJGXKAKC5PzPI62FnjjOGGxAK+/zvJGtXhRYkbFzl2qV5d3SrV47qmNGRel+ILu5s3qlWnG6+Ap7dvLFonXr5sr1B1vLz7H9BWee07u//yMt1HhbRQe7jsLWUaAlG4fhnsoPnVK/ZBKvviQBIAnn5TzfOJtJHyxXbip9MmTQHa2WFmKw4iKpzLMy+7d2tVjxAUHNeBezFNSgPR0sbKUha+NCdy54h9/GNMaSYkZ24bHZbbZ1CvTiGOkHvDjUQAwYYK6Cxla4q3jujO88gpLjxxhf0bFV+8p0ZDS7cPweJCA+mEOfHXQjY4GXniB5f/6S74ORsGMkzhPUa62G83JXX6+fPbRaG3SoQNL1fZCrMRb++Ndd8n5338XJ4cz+NpYrQwdtnevODmcwYxWCO3bs1TNcIO+1kc5lSoVPhb1n/+Ik8UVvHVcdwalT49PPxUnR1n46j0lGlK6fZjq1eUwKsqBwlMcDtkbrpkmC2oxcaKcf/VVcXIUhy8+DGvXls3pf/xRrCxFUcapNlqbcKV7+XLt6vDW1fYqVeS44+++K1aWsvDWNigJ3q8B4PPPxcnhDGYcr3k/2r9fPUsCM14HtejfHxgxguW3bDHenKIomZnyQrKvjClKKlUChg5l+VmzjBspwZfvKZGQ0u3jDB8u55VhOTyB38wWi/d7wy2OSpWAZs1YfuFCWakyAmbcOVGDHj1Yevq0UDFug98roaHGi1PNQ/No6Qzn3DmWeuODn1sSGa3PFcXXJl8WC9CxI8tv3SpWlrIwY9soHYomJ6tTpq8tDCmxWoGxY+XXX34JnD8vTJwyOXhQzvtiewHAqFFy/uOPxclRGr58T4mElG4fR+nsQa2Y3b7kDbcklCalzzwjTo6imHESpwZ8cWnPHuDCBbGyKDFye/CFiuPHtQt/8tdfLDXi/+8po0ez1OFgfjOMii+aGU6axNJTp9juoVEx4yJpeDgQFMTyanlw9sU+qqRiRSApSX5dowawYYMwcUqFP9MaN/bd+d8ddwD//jfLr1oFTJsmVJxi8fV7ShSkdPs4/v7MwzMAzJ2rTpm0gsZCVUVHs/ycOeqt+HuKkZU8LenWTc6vXi1OjqIY+cHXsqWc18I3QVaWnOeWId4ENy8HjBuuDvDN8bpzZzk/Z444OUpDksw7XvN7Wy0HrWa9Dmqi9BcDMBNmNcOyqQX3AVKjhlg5RKPcbHnjDeDMGXGyFAdfEPPle0oEpHQTmDKFpVu3Ajk5npdnZEVCLywWYP16+bXS3EgkZtw5UYOwMNlr8fTpYmVRYmSFJzgYCAlheS3C3/H/3WqVPR57ExYL0KcPy3/1lVhZSsMXFZqQENmh2g8/iJWlJG7eZFYSgDHHh9LgUTzUcqZm5HFST2bMkP3vnDkDPPGEWHmKgx/Z8JWwpCXRqBHw88/y6xdfFCdLcRw+zFJfbye9IaWbwFNPyXk1zrj54iSuOBo3ls8OLljAHMuIxpfbpl8/lu7dq87ikhoYvT24FYyWSnfFioVDlHkTDz7I0pMngYwMsbKUhK8uknKFJT8fOHZMrCzFwdslKEhe/DILvC/FxalTnq/20eIYMUK21Ni/H3j8ceDvv8XKpIQ7z6tcWawcRuCBB4B27Vh+9erCTnZFw5+5rVqJlcPXIKWbQJUqcv6XXzwvj1alGRZL4V2Uzz4TJwvAHOWlprK8L05eHn9cziutEERi9Mkk9/SslpmoEqP/72owcKCcX7VKmBil4qvjNV8QBZhzKqNh9AW50uCLdcojJJ5g5muhNnXqsLEkIADIzmbOWr/5RrRUMrytlEe6fJVy5YDffpPvh3feKXw2XxTKoyt164qVxdcgpZsAIDua+uYbz71t+8Jk2llatJBN7ebOBdasEScLD+NhsTAnd75G/frsIQgA48eLlYVj9Mkkjze9eLH6ZfuCsle1quzbYeZMsbIUR14ecO0ayxu1D2pFYCDw7LMs/9VX2sajdwcz3x93383SzZs9Lys/37dDUBVHhQrMAeDgwez19OlMGTeCxQb3meJr40lJ1KgBbNwov27aVD3/Se6SkcEWbAC6p/SGlG4CgKwYAsDZs56VZXRFQm/eflvOiwwfwdulQgXmQM8Xue8+lhphcgIYf2Jdpw5LtfBCe+kSS719nOALF0b0YH7tmmwOGhkpVhYR8HPdADsCZCTMvHjdvLmc9/Qoz/XrcvQEM14LrejQAXj9dfYsz8tjoQm//575AhBFXp5stkw7qDJ33imHkLx+nZ3NFxlFhY8tAQHyRgShD6R0EwCALl2Y4yTAcxNzoysSetOsGfDhhyy/dq02u4bOQIshzLwLYNfCCB7ljT6x7tKFpZcvq2cqyvn1V5Z6u7LHPQ7/84/xYnbzMSE8HLDZxMoigo4d2blLgI0N/PiNETDzeF2vnpz31JnagQMsDQlh1gmETJcuwMWLsl+ezz4rfGxCb5SLeI0bi5PDiCxaJIfl3bIFqFVLnHWNco7urf5UjAop3UQB4eEsXb7cs3J4/EgzTha0QmlJ8MorYhx5+arnciXK0FRc6ROJ0SfWDRvK+ePH1S2br7AHBKhbrtHgXvMBz8dWtTF6/9OD11+X8zNmiJOjKGZevPbzk/uUp4ub/AysUR0RiiYyEnj5Zdm64OhR4JNPgPPn9Zfl4kWWhoX55iJeafj5sd3u7t3Z4lFeHjvmJsLqji/++vK4LwpSuokCXn2VpVu2yOZcriJJwI0bLE83tEzdurJp+aVLYhQ+mmAzM2nuHIxbH4jE6BNrPz/Z0eLSpeqWzReBevdWt1yjERLCJloA8N13YmUpitEtLfSgZ095TPjXv+Tnl2jM3jbc4/q2bZ6Vw6/DQw95Vo4307UrcPCgfM3HjAH+/W/95di+naVm87avF+HhwKZNwL33stezZgHDhukvx19/sZTaSX9I6SYKeOYZOc9vSldRTljat/dMHm+DO+0BmCft9HR96ze6gqcXI0aw9OJF9eLIuoMkmWNi3aABS9etU7dcX1oE4uf5Dh8GMjPFyqKExgTGtGly/plnZBNZkZj9/uDxf7ki5i5mvw56YbEAc+YAvXqx1/PmsUUkPfsyb2s6J1w648fLTvD27QMee0ybCCEl4XCw1NuPdhkRUrqJAqpWlfPunjvmSkRwMA28RalcGfjoI/m1WjFMncUMCp4eKMM4/fabKCmYwxv+8DNym3BPxO5av5SELyl8PF43IDaCQVFoTGB07SrH0126FEhMFCoOAPPfHzz+r6eLy2a/Dnry0EOFQ5NOmaKvA0e+6XLHHfrVaUZatgS+/loO+7ZoEbBkiX7183GfP9sJ/SClmyjEyy+zVLny7wq0Kl06o0bJMRsHDWKxs/WC2oYRFiabk44eLU4O3h5BQcY28+rZk6Xbt6vniyAz07cUvpo15dBh778vVhYlNCbIKM/b16sn/gwxX5wxa9vwHdfr1z0rx5fGCTVo1apwqLZmzfSLQ79oEUv79NGnPjMTGQns2CFfq1de0W8+QuO+OEjpJgrBH5TuQs66SicoqPCCxu7d+tXNPWVS28g7j6mpnseldxflZNLIHkT5DiCgntOXI0fkPD8z7u3ExrJ03z6hYhSCh4ekMQGoVq3wEaC5c8WZmXMLGACoX1+MDJ5SowZL//7bs3L4Ti31Uee58045akJODvDjj+zMt179mYeaJEqnVSvguefk15995nnIXmf45x+W0j2lP6ZTurOzs9G6dWtYLBbsKzJ7OXPmDAYMGIBy5cqhUqVKeO2115Ajwk20ienbV867cxaLVtDK5tVXgdBQlu/cWX2z3ZJYv56lERH61GdklPF5P/9cjAxmuVciI+VJ1E8/qVMmX3Bo3lybGOBG5K235PzWreLkULJsGUvpbB/j++9lZfHll5niLYKrV+W8WZVuPq6dPeuZRRf3L0PPLdf47jvZ+eXevczke+VK7epT9tk2bbSrx9sYPBjYv19+3bAhc7arFZIkL4TRuK8/plO6x4wZg2rVqt32fl5eHvr374/09HRs2bIFCxcuxNKlS/Hmm28KkNK8cGUQkE2FXIHH1KQVtNKZMEHO//679vVJkhyaieJnsocNvx6jRxfeWdILHoLLDPdKhQos5eEAPYX/71FR6pRnBho1kvNqLV54Cp901awpVg6jYLEAEyfKr4cOZc7v9IYvSkVGmndRip/pBtx3EqVU1lu08EweX8NiAfr1A/r3l8fvadO0i5yivE/4URrCOVq2lK1ssrOBN97Qbk6iPO7RurU2dRAlYyqle9WqVYiLi8Onn35622dxcXE4cuQI5s2bhzZt2qB379747LPPMHPmTKSlpQmQ1rxwT7vz57v+W242GhionjzeyGuvyQscTzyhfX3p6fJ5XLPunKjN99/LeS13AEpi716WmuFeGTqUpVxmT+FGSmb439XCzw948kmWd2dBU20kSba2aNlSrCxG4qmnClu/KK1i9MIbnIcFBLDjVID70VCuXZPztDDkOsHBbFGfh4Ndu5bN7y5cUL8u3sY0v3AdiwX44Qc5tOTPP2s3J+FjS0gI829D6Iu/aAGc5eLFi3jhhRfw22+/IaQYr0Pbtm1DixYtCu2C9+3bF9nZ2di9ezd6cm9ARcjOzkZ2dnbBa66gOxwOOERsfzkBl0sr+Z54woIlS/yRkgKkpzsKdgSdITfXCsAP9evnweEQdFjWJHzzjQVPPumP9HTggw/y8O677l0vZ/pDUhIA2BAYKMFmyxWys2s07r8fAGwAgJEjJfTvr6NXOwA5OexeqVlTvXtFq7GhXz/gjTdskCTg2DGHxxMrLf53M/DUUxbMm+eP69eBs2cdmu8IldYf0tOBrCzW/8PCHDQmKBgyBFi1yoq4OD/8+Sfw2mt5+PTTfN18L1y8aAHgj8jIfDgc6pw/0nreUBzly/sjK8uCP//Mw333uX6f8+dWhQoSJImeW+7y4otAdrYfvv3WD2lpFsTGSoiK8sPAgeVU6w+bNrExvVIl9fqsr/Hpp0CnTmxMfvBB4PRpR6GoQmpw4AAbWypVkuBwsDmPiLHB23D22plC6ZYkCcOGDcPLL7+M9u3bI7GYeB7JycmIKmKrGBERgYCAACQnJ5dY9qRJkzBu3Ljb3o+LiytWuTcS8fHxmpSbmekPoD8A4PPPd6Bly8tO//bUqbsARCAnZyfs9ouayOcthIQAISGxyMiw4cMPrbjjjpXw93ff00lp/eHEiXAAPVCuXBZWrdI5VpmBeeihpli6tBFOn7bgt9/+QECAfgrgsWOdAUQhMPAA7PYzqpat9tiQl2cBcD8AYMKEf/DII/94VN7Ro+x/DwhQ/383MtnZVgD3AQAmTDiC2NhEXeotrj9cuhQMIAb+/vnYtMluaGd+InjyyQCsWxeD3Fwrpk+3olmz9aha1cP4V06ycWMtAG2Qm5sCu93NbeIS0GreUBxRUd1w+XIlHDx4Fnb7/rJ/UIQjRyIBdEdQUDrs9rXqC+hDdO0KxMV1wb59VbB/vwWAFaGhtVC58hrYbJ57WLtwoROAaJQvfxZ2+z6Py/NVBg9ujEWLmgAAxo8/ivvuS1C1/JUr6wFoiatXHbDbVxX6TM+xwdvIcDLchUWSRPnnBMaOHVuswqtk586d2Lp1KxYtWoRNmzbBarUiMTERdevWxd69e9H61qGEF198EadPn8bq1asL/T4gIABz5szBY489Vmz5xe1016xZE5cvX0aYQW0vHA4H4uPj0adPH9hsNk3qqFPHHxcuWPDkk/n48UfnVy0bN/ZHQoIFmzblonNnYV3LNCQmAo0asTbs2zcfK1a4vkLsTH+Ij7egf39/tGwpYfdufXd0jUxWFhAWxq5Zjx75iIvTb4W+a1crdu3yw9KluRgwQJ17RcuxYcgQK5Ys8UOFChJSUjzrQwEBTDY1/3ezcO+9Vqxbx052ZWU5CkIIakFp/WHvXrarUrWqhNOnaUwojqNHgVat5Ot24oQDtWppX++DD1qxcqUfnn46H99/r95Ot9bzhqJ88okf3nnHitBQCVeuuN7HPv7YD++/b0XHjvnYsoV2Tz3l2jVg82YLFi/2w6JFbOCxWCSMH5+PMWM8W3DmY/q8ebl49FHfGtPVJDsbuOMONo8GmGVZvXrqlT9+vB/Gj7fioYfy8f/t3XlYVdX6B/DvYUYmRRQ1QM1QUREHTJHMTNPUTLI0zUytNG6aGtpg2pXUpK5DYqWplUOl9rs53ism5mylJYpDch1zyDnnKWTYvz9Wi72PTAc4+2zOOd/P8/DsdeaXwz6bs/Za630XLxafKSOODY7m+vXrCAoKwrVr14rsOxo60j106NBCO8NSrVq1MHHiRGzfvh2e9ywAjI6ORt++fbFgwQJUq1YNO+5ZOHTlyhVkZWXlGwHX8vT0zPe8AODu7l7udz49YwwPF+t+jhxxgbu75d8KZVmq4GA3lPO3r1wIDxcJZ/bsAdaudcGdOy6lXmdT1P5w9arYBgWZyv1+bUvu7sCDD4p6mZs2ueDyZRebJfeS2V6rVbP+Z0WPY0ObNsB33wFXr5rg5uZe6pFRbYm28HDnO0784x/Ahg2ifeGCu03Wqha0P8iEOpUr85hQmMaNgVdfBWbOFJdnz3bHhx/qX+JPfiVRlJL9/7WELb/byBMU2dml28fkeMi1a9Z/H5xR1arA008DVaoAK1cq+OsvExTFhEWLXPHQQ66IjQXcStEr0A7dRUQ43zHdmtzdxTTzp58Wl+fNc8cHH1jv+U+fFtu6dfN/puyh31NeWfq+GXoUCwoKQv369Yv88fLywowZM7Bnzx6kp6cjPT0dKSkpAIBvv/0W77//PgAgJiYG+/fvx1mxCAiAmCLu6emJ5tpCs2SR+Hix3b7d8nIf5zWzye05AYytbd6stuPi9HkNeylPZQTZAQJEqRNbzf3R1um2B927q+0ffij98xzWzEzXZvR2Fs88IxIcAaIuq1F4TLDMp5+qpTQnTzY/XutF/m1kbXd71bKl2N65I3IIlJR8H4oZm6ESevhh4MKFbIwbJ2oXHjgAPPKI6OyVhvYzUb9+2eNzdj16iDXdAPDhh2Wvda8lq+WwBJ8x7OLUYVhYGBo1apT3U/fvb2p16tRByN9FNTt27IgGDRqgX79+2L17N9avX49Ro0Zh0KBB5XaaeHnWsaPazsiw7DFHj6pt1v+zXECA+qVu40Z9avg6QjZcvfj4AMOHi/bZs8CsWfq/ZlYWIIsq2EunRzuttixn3rUpOZwpe7nWAw+IbXKyOppna7I2rL3sf0Z6+221/cILgIXL90rNUY7Xcj8H1FlwJSFPiHIftT4vLyAy8k/06pWbV5t+9OjSlTDdskVtl/NUSHYjIUFta48/ZSW7Q/JvTrZlF51uS7i6umL16tXw8vJCbGwsevXqhbi4uALLi1HxAgPVaUZz5lj2GPlFoUULfWJyZPPnq+0XXrD+89vbqKqtffSROPkBAEOGmJ9A0oO29Ja9nHE2mUT9UEB8Gb5zp3TPI48ThRSUcApjxqjtFSuMiUHWSudswuI98ogo8wgAp07pX/LNUY7XJpP6O5SmbJh8H2R5TbIuNzcFX3+dg3/9S73umWdKfiJQDszI0Vkqu4ceEiPeALB8ufW+k8j/v1FR1nk+Khm77HTXqlULiqLkJVGTwsLC8N///he3b9/GpUuX8PHHHxe4XpssI6e2ffaZZfeXZzvt/YuCEapVU0cPjx4FvvrKus+/dKnY8m9TMJNJfY8AUU5s2TL9Xu/CBbXt6qrf61jbm2+q7dRSJsHn6JX5MpLevc3XudvK3bti27Ch7V/bHr31ltp+8UXgyBF9XicnB5AFVxzhM1K1qthu21byx8p9VE5TJ3089ZSYxgyIDnerVmrOEUssWSK29epZPzZnpp1RNmlS2Z/v7l2RTA9wjGOLPbLLTjfZhlxHlZ1t2TrXmzfFtjRrtwgYMUJtv/OOdZ9briH19bXu8zqS9u3V5CUHDoj1m3/9pc9rybPN2mUc9kBbV/o//yndc8j3VK/31h54egJJSerl/fttH4PcBxs1sv1r26MaNYDp09XLCxbo8zrafcFWSR31FB4utiUdPc3MVL9T6F3P3tl5eYkTqpGR4nJ6uuUnnbXlidu0sXpoTi08HGjWTLTXrCn6vpbQzrBjp9sY7HRToZ58Um1rP6yF+eab/I8jy3l6AqtXi/Yff4jRFGuRZ635T7FoCxYA06aJ9vbtQMWKwKZN1n8de16zOW6c2H7xRclGQyR5nOjWzXox2aNRo9R25862f3173geNMnQoEBsr2hMnlm79a3G0U8sdYaLeE6IsPRYvLtnjZPIoFxd16Q/pa9Mm8T8PAAYNAlatKv4xW7eqbZmbhqxH5pg5e1b8zy0LOdskJKR0Weqp7NjppkJpE2JoE2UURo5c8ax06T36qHownDdPLfVVFtnZ2vJAZX8+R+bjA7z0ElC7tricmQnMnl269YhFkUmF7PHvIWcDAKWfYg7AJqWyyjM3N2DwYNE+c8Y8wZwtyCzy9rgPGsXVFZgwQb38z39af2mAPBnSoIF1n9copa1rLjvdubnQtZY9qQIDgY8/Vi+//ro626Aw69erbXtaKmUvtKtotbOjSkPOQmUeD+PwUEaFMpmAvn1F+/PPi77vrVvqNCOuvyo9Ly/zL98DBpT9OeVopMlkP0m7jOTvL9bVy2RXS5aINW47d1rvNWRnVY4q2JPISPF+AKKGcUloR8abNrVeTPZqxgy13bSp7dZ2y9FUQNTsJcu1a6euf929W9SutyZHm4Gg7TRo97virFsntva2BMfePf+8WFoFAMeOmS97K4hcazxokK5hOS0PD+Drr0X76FFg797SP5f8/LEEn3HY6aYiycQYv/1W9P1On1bb2jIhVHL33aeeuFi5Ukw1Lwv5+IoVOaXIUiaTOOHRtat6oqJTJ7XMUln5+Iht9erWeT5be/ZZsb1yRU36ZInt29U2Z8SI6cOJiaJ99ap1kuVY4tgxtc1Od8n16aO2//EPy3KeWOrnn8XWUWYgaNelaz//xTGZxJajcran7ZR98UXhJ5a03/ucfbmQnrp2VdvaE7Ul9cMPYusoJ/TsETvdVCRtCYiTJwu/nzyDVru2+s+SSk+7/u2jj8r2XGlpYsupXyXzwANizab8DFy+bJ69uyzk56V5c+s8n60NHKi2P/3U8sd9/73YOvvUcq1x49QMz+++K5L46U2Opt5TAIQsFBoKvP++aF++DOzaZb3nPnNGbB3pBKmcYi4//5aQx0iuE7a9kBDgl1/Uy337FpwIT+Y/AYAOHfSPy1lVrKjmSipLAke5zFCe9CfbY6ebiqTNbFvUh11mMnaUs/NGq10biIkR7WnTypbd+MYNsWVHp3TGjxdr7QExLVx+2S6t3FxR6xew389LQICoIwqIhFKWJlRLSRFbJvQzt3y52m7YUB2R0MvmzWLLEY/S0y6t6NDBeksD7twR2+ho6zxfefDww2IrP/+WkKOr9nqMtHfR0cCXX4r23buirrN2ecD582qnu3NntUIK6UMmMM3OLl1yV0VRT+hxCahx2OmmYtWoIbZFTQ2To+DyCwOVnbZzt3Bh6Z9HjmrJThKVzH33mb//Y8eWbTrpwYPmz22v3n5bbVtazkQmRJJlUEho3VrNnwGIEz3aUjzWdvu22F6/rt9rOLqKFYGXXxbtq1eBffus87xyuYYjLdOSM3pKkhDNw0NsuQzFGCaTmNEkZ8McPCjym1y+LJLmamfjjRxpSIhOJSpKbZcmi7lM3go41rHF3rDTTcX64AOxTUkBcnIKvs+iRWI7bJhtYnIG7dqpa2cnTy4+i2hh5N+GIwald9995glMtGs6S0qOFgQGisR59qprVyAiQrSff774+1+9qmbMbtdOt7Ds1pdfivXBgCjD07Chfh1veSJOm4meSm7mTLXdpIkYhSqLrCx1vb0jHa/bthXbo0dFHojiXLokRlcBJlw02ubN6sDLa6+J/dLbW2Q2B4AWLYD27Y2Lz1m4uqo5P77+Wq0WZKlffxXbChUAX1/rxkaWY6ebiqVdq3PiRNH3rVVL11CcjvzHBqg1FksqMFBs/fzKHo8za9RI3b+//bZkmXi1ZPKZunWtEpahtCfZ5NS1wsh/+oDjlEOyJg8PYPRodRnI4cOiI65HRvP0dLHVJrmiknN3V6d9AiKbeVlok2Y6wvFB0n7e5b5XFO1yKnus8OBI/P1FXhlf3/z5evz8zGc8kb6eeUZtl7SaipxhZ82kj1Ry7HRTsapXV//xaRNnSNrOBxPzWFfLlupoYnGlOwoj/z6cXl42JpN5vfrSlkjZsEFs/f3LHpPRZJ1pAOjfv+j7yrInzZrZ9wi/nkJDxVIdmXgqPl7Nl2FNMlkbOzRll5iojko/91zZnktmLq9aVZ1e7Qg8PdU16pasR5U5B5o2ZWLW8qBXL5EbZs4c9bqHHhLLU3r0MC4uZxMeDtSsKdraARlLfPWV2LJcmLHY6SaLyNJhBWUq/vFHtS2z8JL1yOzZBw+WbnRVrrd3pOmKRgkNVU9erFhRdEb/wsgvkRUqWC0sw7i4qGfff/ih6Pdj9WqxDQnRPy57J5f0AEBcHLBxo/WeOydH3Qc5ddc65Am4I0fMczaUlMwuXNgyLnsmq2dYsi/L0XBHyuDuCHr0EEuJunQRlRbI9p54Qmx37hRLtix15IjYcmDMWOx0k0W0pZLOnjW/TWba5ZdpfQwZorbnzy/ZY48eVducSmod2r/Bxx+X/PFyPa2jlFjRdhALez8URf29e/XSPyZ716eP+Zfa116z3nNfuaJOMbTXOvHljfb/Y1lqrcvPiLZUp6OQFSC0/5MKI08uywoeVD4EBooR09WrgY4djY7GOSUkqG2Zr6c42gTHLMFnLHa6ySKyRiBgXjpMUYBPPhHtxx6zbUzOokYNkVQJAN54o2RrPLUZ55k8wzrq1FFL4EyZUvKEJkuXiq2jzDy49/3QJpyTtFNKu3e3SVh2LyFBTaz222/AK69Y53n/9z+xDQgQa5Kp7CpVAvr1E+2FC4FDh0r3PLJ0nKMcG7Rk0r4zZ4Bbtwq/X26uSCQIiKz+RKS6/351+dGQIZat0Zaj3IBj5YqwR+x0k0Xc3NSEXKmp6vUXL6ptjmDpZ+xYtS0zQFvi/HmxDQiwbjzOLjFRbZc0wZ0sm+NIo4za96OgJSj/939qmyd/LFOxoqhaIBMgzpljfrwtrePHxVZOZSbreOMNtS3zF5SUnIItt45E5iYBzJek3UvunwDQqpVu4RDZLe3/W23yxcJoTwIyR4Kx2Okmi02dKrYbN6rrsrTTSR9/3PYxOQtt8ouSjHjJ6YplKXFF+bVrp3aaH3vM8oyg58+rMxW0dTftXbt2atmwOXPMp70dPQp89ploM9Ntyfj4mHdCatZUSymV1qpVYhsXV7bnIXORkUDPnqI9YYJ6wtNSiqJm+HfEWWMVKqjrSYs6Dvzyi9h6ealJo4hINWCA2rZk6ZE8Ee6IxxV7w043WUy7hmfMGFGTVE4tl3UcST/ypMbmzZZ38vbtE9tq1fSJyZmNGqW2L1yw7DG//aa2HW32gbZ82MCBIkN2Zibw1lvq9ZwNU3KBgcDQoaJ95w6wbFnZnu/cObHl1HLrk38nwPL1ltLNm2rbUUvqycR9RZVWk5n1HXG0n8gaTCY138HKleK7eFHk90Dm9TEeO91ksRo11C/QP/8s1hzK7IllSR5Dlpk8WW0XNT1PS9ZyZI1u60tIUL8YypNPxZElxxyxFE6LFurMl7t3ReKkefPUNew9ezJbdml9/LE66tenT9mmmcvM0Bz1sL6HHwYeeUS0ExJKloV81y6xdXEBqlSxemjlgrbs5enTBd9HLkXR1iQmInPJyWp7xYrC73fjhpqYsLiynqQ/drqpRLRTm2XdP4BTFW1BJlMDxPRFS8hM8/XrWz8eUt/XiRMtm30g8yE4ap3q3r2BZ58V7fPngenT1du0I95UctOmqe1Bgyyf7XKvGzfENjKy7DFRfv/8p9q2pCa1lJYmtrm5jndCTtLuc/JknFZWllpyrUUL28REZI+aN1fbL75Y+P8DbYm+tm31jYmKx043lUjt2sDIkaItR7nHjHG8qbLlkckEvPCCaKemFv+l+/Jltc0RRn1o1yYWlLX7XrKOtaP+8wsKAhYvBqpWFZflF+hPPzX/kkAl16OHWiN+5Upg//6SP8epU2qbJ+L00a6d2p4xw/LHybX7jpyx22QCwsNFW+YW0Fq/Xm137mybmIjskYsLMH68aN+4Ufj3DzlzxM+PS4rKA3a6qcTGjhXJkpKTgblzzWuUkr4mTlTbxY2iaA/CjpQpuzzRrlEu7nOQm6tOqXTktc0mkzgplJwsfhYsEGu8qewWLlTbbdqUrHwgoCbqAkR2dNKHrLG+apW6hr44K1eK7YMP6hNTeSETP61fnz/z8rx5YuvpKUojEVHhtMs1CkqWm5MDfPONaMfH2yQkKgY73VRiFSuK6Y3DhgEvvwz4+xsdkfMICVHbBU3P09JmPSZ9eHgA7duL9g8/FH3fY8fUtqOPMkZFiePDsGFidoa3t9EROYbatcUxFxAlv2TSKUvJ5SYeHtaNi8zJmt2AWnu7OHLmkqNn7O7WTW1/9535bbIcplwXT0SF8/NTO9sZGSLRplZGhtrWVsAh47DTTWRHTCbgnXdE+9NPgevXC7+vXE8ry9iQPmQ5rNxcYMmSwu/37bdiGxTETiiV3uzZajsqqmSj3fJEnSzvRvoID1dPxr36KnD7dtH3v3RJnfr/8MP6xma0WrWALl1E+/XX1RwDu3erWc2HDDEkNCK7s2CB2m7Txvw2eYI2IABo1sx2MVHh2OkmsjPaqck//VT4/eT0ck4t15d2GmRSUuH3kxnn2eGmsnBxARITRTs3F9i61fLHHjoktp6eVg+L7jF8uNretq3o+27frradIcGdtrbwp5+KkkcJCep193YeiKhg7u5qOdm0NCAlRbTT04EdO0RbngAk49lVp3v16tVo2bIlvL29ERQUhB49epjdfvLkSXTr1g0+Pj4ICgrCsGHDcPfuXYOiJdJHVBRQt65oF5ao5+JFdbpiQWt9yHpcXNTM0nv3mier0pLTz7UVAIhKY9w4wMdHtJ980rLHZGaqOQU6ddInLlJ16waEhYn26NFF31cmO2rSxDmSHT3+uJrcc/RoYMoUNUfJyy8z3wBRSXz7rVrxoGtXMa1ce4x//31j4qL87KbTvXTpUvTr1w8DBw7Enj178OOPP+K5557Luz0nJwddu3bFrVu3sG3bNixZsgRLly7FSJlqm8iByDVva9YAt27lv107+uXoiXnKA+10XZlRVCszU5TDAdSz0kRlMXWq2F6/rnbaiiIz5wPcB22lb1+x3bVLnWVQELm2WZ5MdQZTpqhtbc3hUaNsHwuRPfP3B2bOVC8PHAhcuCDao0cD9eoZExflZxed7uzsbAwfPhyTJ09GfHw86tati3r16uGZZ57Ju09qaioOHDiAr7/+Gk2bNkWHDh0wdepUzJ07F9eLWvhKZIcGDVLba9bkL+oqpzNWrixGYklfVaqopX6+/DJ/OTdZnxswr7dOVFpyvR4AvPFG8fdPT1fbnF5uG9qMwdovxVq5ueqa76ee0j+m8uLRR4Hu3UVbZngfP54dBKLSiI9X8/fIaeXBwWKU25T/KyIZxM3oACyxa9cunD59Gi4uLmjatCnOnTuHJk2aYMqUKWj49zfYn3/+GY0aNUKNGjXyHtepUydkZmYiLS0N7bTFMzUyMzORmZmZd1l20LOyspAlh6bKGRlXeY2P9BcVBQQEuOHaNROee84NS5eazPaH5GQ3ACY8/XQOsrJKWFeISmXuXKBhQ3fk5gIffpiDkSPV9338eFcALggLU+Dqmg29Pro8NjiXpUtNePppN5w8CXz1VTZ69zY/26PdH5KSxDGhUSMFWVnZBkTrfKpXB+LiXLFihQuSk4GhQ7PyZScXycPEnPIuXbKc6tjwySdAt24mZGcDFSoATz6p6Pb7k7nyuD9Q2UyfDnTqJD5PABATo+S1i8J9oewsfe/sotN97O9aO4mJiZg2bRpq1aqFqVOnom3btjh06BACAwNx7tw5BAcHmz2uUqVK8PDwwLkiCmUmJSXhvffey3d9amoqKlSoYN1fxMrWrVtndAhkoGeeuR9ffCGy7pw44Ze3PygKkJsrhhC8vdORkvJHoc9B1iNGt8X7/u67QHj4Gri5KVAUYOdOcX2dOqeQkrJb91h4bHAOubkmAGJR95tv/gU/v/UFjmqsW7cOGRldAbghOPgkUlLSbRmmU2vVqhJWrBApySdMOIQePY6Y3b58+QMAxODBxo0pusdT3o4NQUFqW67rJtspb/sDlY328/T77+LHUtwXSu92cSUq/mZSlHsnQtpOYmJigR1erV9//RWHDh1C3759MXv2bAwePBiAGKEOCQnBxIkT8corr2Dw4ME4ceIE1q5da/Z4Dw8PLFy4EL0LKVJX0Eh3aGgo/vzzT/iX0wLUWVlZWLduHR577DG4O0PWFSpUQIAb7twxoWLFv3D6dC7c3d0xY4YLRo1yBQCcO5eFwECDg3QiGzea0KmTOJfZq1cuvv46B//6lwvGjhV/jz17shARod/r89jgfBYtMmHAALHPTZ2ag9deU2dYyP2hQYOOqFtXpM3fty+LU3htLC7OFSkpYp3PxYtZCAgQ1+fkAN7e4nP6/PO5+PLLHN1i4LGBtLg/kMR9oeyuX7+OoKAgXLt2rci+o6Ej3UOHDi20MyzVqlULN/4u5NigQYO86z09PXH//ffj5N/ZYapVq4YdciHD365cuYKsrKx8I+Banp6e8CxggZu7u3u53/nsIUbS1yuviClFV696IT09Gw8+6Ja3vtNkAoKDuX/Y0sMPi2mSt28Dmza5YOpUF/z73+rtjRq522SNPY8NzqNbN7U9cqQr+vVzRZUq5vdZt84jr92ggW32QVIlJKilfGbMcM9LtpiRod5nwAAXuLvr/4fhsYG0uD+QxH2h9Cx93wz91xsUFIT69esX+ePl5YXmzZvD09MTBw8ezHtsVlYWjh8/jpp/L5CKiYnB/v37cfbs2bz7pKamwtPTE82bN7f570ZkCxMmqO0nn3TF6tVqEq/58w0Jyal5eakJqy5cEJlD9+wRl//9bya1I+sLDAQWLlQvF5Q9f+5csePFxHAfNEL79oD8GjJhAnD4sGj/859i6+HBWrpERI7OLtZ0+/v7Iz4+HuPGjUNoaChq1qyJyZMnAwB6/p2ur2PHjmjQoAH69euHyZMn4/Llyxg1ahQGDRpUbqeJE5WVry+QnJyD4cNdcemSCW+/rd52Txl7spHwcOCjj4B9+9TrqlUDnnjCuJjIsfXoIUZT//xTJKfq3RuIjRW3nT7ti/R0sdDbmbJjlzfTpwNt2oj2c88BlSoBaWnicjET/oiIyAHYRacbACZPngw3Nzf069cPd+7cQcuWLbFhwwZUqlQJAODq6orVq1fj1VdfRWxsLLy9vfHcc89hirYYJJEDio/PRWJiFq5c8cL//ieue/dd0SEnY4wYYXQE5Ex8fIClS4G2bcXlKVPUTndqqpouW5aUIdt76CFxcmTZMmDnTvPbEhKMiYmIiGzHbjrd7u7umDJlSpGd6LCwMPz3v/+1YVRExjOZgEmTtsLbux3c3NxQoQLw+ONGR0VEttSmDTBkCPDpp8CKFUDFioDJ5IarVx8AADz9NFCrlpER0ty5QK9eMCvjExYmSkASEZFjs5tONxEVrnr12+jSRQFzYBA5J5MJeOstsb77xg3g2jUAUOuHcTTVeIGBwLPPGh0FEREZgSlViIiIHEBoKHD+vFheonXxYhZatzYmJiIiIuJINxERkcPw9gYGDAA2bAAuXVLQuPEhBATcb3RYRERETo2dbiIiIgdy//3Atm1AVlY2UlL+B4CdbiIiIiNxejkRERERERGRTtjpJiIiIiIiItIJO91EREREREREOmGnm4iIiIiIiEgn7HQTERERERER6YSdbiIiIiIiIiKdsNNNREREREREpBN2uomIiIiIiIh0wk43ERERERERkU7Y6SYiIiIiIiLSCTvdRERERERERDpxMzqA8kZRFADA9evXDY6kcFlZWbh9+zauX78Od3d3o8Mhg3F/IIn7AmlxfyCJ+wJpcX8giftC2ck+o+xDFoad7nvcuHEDABAaGmpwJERERERERFTe3bhxAwEBAYXeblKK65Y7mdzcXJw5cwZ+fn4wmUxGh1Og69evIzQ0FKdOnYK/v7/R4ZDBuD+QxH2BtLg/kMR9gbS4P5DEfaHsFEXBjRs3UKNGDbi4FL5ymyPd93BxcUFISIjRYVjE39+fHxDKw/2BJO4LpMX9gSTuC6TF/YEk7gtlU9QIt8REakREREREREQ6YaebiIiIiIiISCfsdNshT09PjBs3Dp6enkaHQuUA9weSuC+QFvcHkrgvkBb3B5K4L9gOE6kRERERERER6YQj3UREREREREQ6YaebiIiIiIiISCfsdBMRERERERHphJ1uOzRz5kzUrl0bXl5eaN68ObZu3Wp0SGSALVu2oFu3bqhRowZMJhNWrFhhdEhkkKSkJLRo0QJ+fn6oWrUq4uLicPDgQaPDIgPMmjULjRs3zqu5GhMTgzVr1hgdFpUDSUlJMJlMGDFihNGhkAESExNhMpnMfqpVq2Z0WGSg06dP4/nnn0flypVRoUIFNGnSBGlpaUaH5bDY6bYz3377LUaMGIExY8Zg9+7daNOmDTp37oyTJ08aHRrZ2K1btxAVFYVPPvnE6FDIYJs3b8aQIUOwfft2rFu3DtnZ2ejYsSNu3bpldGhkYyEhIfjggw+wc+dO7Ny5E48++ii6d++O3377zejQyEC//vor5syZg8aNGxsdChmoYcOGOHv2bN7Pvn37jA6JDHLlyhXExsbC3d0da9aswYEDBzB16lRUrFjR6NAcFrOX25mWLVuiWbNmmDVrVt51ERERiIuLQ1JSkoGRkZFMJhOWL1+OuLg4o0OhcuDixYuoWrUqNm/ejIcfftjocMhggYGBmDx5Ml566SWjQyED3Lx5E82aNcPMmTMxceJENGnSBNOnTzc6LLKxxMRErFixAunp6UaHQuXA22+/jR9//JGzZW2II9125O7du0hLS0PHjh3Nru/YsSN++ukng6IiovLm2rVrAERni5xXTk4OlixZglu3biEmJsbocMggQ4YMQdeuXdGhQwejQyGDHT58GDVq1EDt2rXRu3dvHDt2zOiQyCCrVq1CdHQ0evbsiapVq6Jp06aYO3eu0WE5NHa67ciff/6JnJwcBAcHm10fHByMc+fOGRQVEZUniqIgISEBDz30EBo1amR0OGSAffv2wdfXF56enoiPj8fy5cvRoEEDo8MiAyxZsgS7du3iTDhCy5YtsXDhQqxduxZz587FuXPn0Lp1a1y6dMno0MgAx44dw6xZsxAeHo61a9ciPj4ew4YNw8KFC40OzWG5GR0AlZzJZDK7rChKvuuIyDkNHToUe/fuxbZt24wOhQxSr149pKen4+rVq1i6dCn69++PzZs3s+PtZE6dOoXhw4cjNTUVXl5eRodDBuvcuXNeOzIyEjExMahTpw4WLFiAhIQEAyMjI+Tm5iI6OhqTJk0CADRt2hS//fYbZs2ahRdeeMHg6BwTR7rtSFBQEFxdXfONal+4cCHf6DcROZ/XXnsNq1atwsaNGxESEmJ0OGQQDw8PPPDAA4iOjkZSUhKioqKQnJxsdFhkY2lpabhw4QKaN28ONzc3uLm5YfPmzZgxYwbc3NyQk5NjdIhkIB8fH0RGRuLw4cNGh0IGqF69er4TsREREUzMrCN2uu2Ih4cHmjdvjnXr1pldv27dOrRu3dqgqIjIaIqiYOjQoVi2bBk2bNiA2rVrGx0SlSOKoiAzM9PoMMjG2rdvj3379iE9PT3vJzo6Gn379kV6ejpcXV2NDpEMlJmZiYyMDFSvXt3oUMgAsbGx+UqLHjp0CDVr1jQoIsfH6eV2JiEhAf369UN0dDRiYmIwZ84cnDx5EvHx8UaHRjZ28+ZNHDlyJO/y77//jvT0dAQGBiIsLMzAyMjWhgwZgkWLFmHlypXw8/PLmw0TEBAAb29vg6MjW3rnnXfQuXNnhIaG4saNG1iyZAk2bdqE77//3ujQyMb8/Pzy5XXw8fFB5cqVme/BCY0aNQrdunVDWFgYLly4gIkTJ+L69evo37+/0aGRAV5//XW0bt0akyZNQq9evfDLL79gzpw5mDNnjtGhOSx2uu3Ms88+i0uXLmH8+PE4e/YsGjVqhJSUFJ6ZckI7d+5Eu3bt8i7LNVn9+/fH/PnzDYqKjCBLCD7yyCNm18+bNw8DBgywfUBkmPPnz6Nfv344e/YsAgIC0LhxY3z//fd47LHHjA6NiAz0xx9/oE+fPvjzzz9RpUoVtGrVCtu3b+f3RyfVokULLF++HKNHj8b48eNRu3ZtTJ8+HX379jU6NIfFOt1EREREREREOuGabiIiIiIiIiKdsNNNREREREREpBN2uomIiIiIiIh0wk43ERERERERkU7Y6SYiIiIiIiLSCTvdRERERERERDphp5uIiIiIiIhIJ+x0ExEREREREemEnW4iIiIDJCYmokmTJoa9/rvvvovBgwcb9vrWsGnTJphMJly9erXY++7btw8hISG4deuW/oERERFpsNNNRERkZSaTqcifAQMGYNSoUVi/fr0h8Z0/fx7Jycl45513DHl9I0RGRuLBBx/ERx99ZHQoRETkZNjpJiIisrKzZ8/m/UyfPh3+/v5m1yUnJ8PX1xeVK1c2JL4vvvgCMTExqFWrliGvb5SBAwdi1qxZyMnJMToUIiJyIux0ExERWVm1atXyfgICAmAymfJdd+/08gEDBiAuLg6TJk1CcHAwKlasiPfeew/Z2dl44403EBgYiJCQEHz55Zdmr3X69Gk8++yzqFSpEipXrozu3bvj+PHjRca3ZMkSPPnkk2bXfffdd4iMjIS3tzcqV66MDh06mE3FnjdvHiIiIuDl5YX69etj5syZZo//448/0Lt3bwQGBsLHxwfR0dHYsWNH3u2zZs1CnTp14OHhgXr16uGrr74ye7zJZMLnn3+Op556ChUqVEB4eDhWrVpldp+UlBTUrVsX3t7eaNeuXb7f88SJE+jWrRsqVaoEHx8fNGzYECkpKXm3d+rUCZcuXcLmzZuLfH+IiIisiZ1uIiKicmLDhg04c+YMtmzZgmnTpiExMRFPPPEEKlWqhB07diA+Ph7x8fE4deoUAOD27dto164dfH19sWXLFmzbtg2+vr54/PHHcffu3QJf48qVK9i/fz+io6Pzrjt79iz69OmDF198ERkZGdi0aRN69OgBRVEAAHPnzsWYMWPw/vvvIyMjA5MmTcK7776LBQsWAABu3ryJtm3b4syZM1i1ahX27NmDN998E7m5uQCA5cuXY/jw4Rg5ciT279+PV155BQMHDsTGjRvNYnvvvffQq1cv7N27F126dEHfvn1x+fJlAMCpU6fQo0cPdOnSBenp6Xj55Zfx9ttvmz1+yJAhyMzMxJYtW7Bv3z58+OGH8PX1zbvdw8MDUVFR2Lp1a1n+TERERCWjEBERkW7mzZunBAQE5Lt+3LhxSlRUVN7l/v37KzVr1lRycnLyrqtXr57Spk2bvMvZ2dmKj4+PsnjxYkVRFOWLL75Q6tWrp+Tm5ubdJzMzU/H29lbWrl1bYDy7d+9WACgnT57Muy4tLU0BoBw/frzAx4SGhiqLFi0yu27ChAlKTEyMoiiKMnv2bMXPz0+5dOlSgY9v3bq1MmjQILPrevbsqXTp0iXvMgBl7NixeZdv3rypmEwmZc2aNYqiKMro0aOViIgIs9/1rbfeUgAoV65cURRFUSIjI5XExMQCY5CeeuopZcCAAUXeh4iIyJo40k1ERFRONGzYEC4u6r/m4OBgREZG5l12dXVF5cqVceHCBQBAWloajhw5Aj8/P/j6+sLX1xeBgYH466+/cPTo0QJf486dOwAALy+vvOuioqLQvn17REZGomfPnpg7dy6uXLkCALh48SJOnTqFl156Ke81fH19MXHixLzXSE9PR9OmTREYGFjga2ZkZCA2NtbsutjYWGRkZJhd17hx47y2j48P/Pz88n7XjIwMtGrVCiaTKe8+MTExZo8fNmwYJk6ciNjYWIwbNw579+7NF4u3tzdu375dYJxERER6cDM6ACIiIhLc3d3NLptMpgKvk9O2c3Nz0bx5c3zzzTf5nqtKlSoFvkZQUBAAMc1c3sfV1RXr1q3DTz/9hNTUVHz88ccYM2YMduzYgQoVKgAQU8xbtmxp9lyurq4AREe2ONrOMgAoipLvuqJ+V+Xvqe5Fefnll9GpUyesXr0aqampSEpKwtSpU/Haa6/l3efy5cuoU6dOsc9FRERkLRzpJiIislPNmjXD4cOHUbVqVTzwwANmPwEBAQU+pk6dOvD398eBAwfMrjeZTIiNjcV7772H3bt3w8PDA8uXL0dwcDDuu+8+HDt2LN9r1K5dG4AYoU5PT89bf32viIgIbNu2zey6n376CRERERb/rg0aNMD27dvNrrv3MgCEhoYiPj4ey5Ytw8iRIzF37lyz2/fv34+mTZta/LpERERlxU43ERGRnerbty+CgoLQvXt3bN26Fb///js2b96M4cOH448//ijwMS4uLujQoYNZJ3jHjh2YNGkSdu7ciZMnT2LZsmW4ePFiXqc4MTERSUlJSE5OxqFDh7Bv3z7MmzcP06ZNAwD06dMH1apVQ1xcHH788UccO3YMS5cuxc8//wwAeOONNzB//nx89tlnOHz4MKZNm4Zly5Zh1KhRFv+u8fHxOHr0KBISEnDw4EEsWrQI8+fPN7vPiBEjsHbtWvz+++/YtWsXNmzYYNaxP378OE6fPo0OHTpY/LpERERlxU43ERGRnapQoQK2bNmCsLAw9OjRAxEREXjxxRdx584d+Pv7F/q4wYMHY8mSJXlTt/39/bFlyxZ06dIFdevWxdixYzF16lR07twZgJi2/fnnn2P+/PmIjIxE27ZtMX/+/LyRbg8PD6SmpqJq1aro0qULIiMj8cEHH+RNP4+Li0NycjImT56Mhg0bYvbs2Zg3bx4eeeQRi3/XsLAwLF26FP/5z38QFRWFzz77DJMmTTK7T05ODoYMGYKIiAg8/vjjqFevnllps8WLF6Njx46oWbOmxa9LRERUVibFkkVSRERE5DAURUGrVq0wYsQI9OnTx+hwbCIzMxPh4eFYvHhxvqRuREREeuJINxERkZMxmUyYM2cOsrOzjQ7FZk6cOIExY8aww01ERDbHkW4iIiIiIiIinXCkm4iIiIiIiEgn7HQTERERERER6YSdbiIiIiIiIiKdsNNNREREREREpBN2uomIiIiIiIh0wk43ERERERERkU7Y6SYiIiIiIiLSCTvdRERERERERDphp5uIiIiIiIhIJ+x0ExEREREREenk/wEIlMjXiyhLEgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_combined_ppg_signal(normalized_ppg_data,1,True)\n",
    "plot_combined_ppg_signal(ppg_data,1,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "967eb1ec-189e-48bf-b142-c00d718f6e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_bgl_ecg_columns(df):\n",
    "    total_rows = len(df)  # Get total rows (e.g., 219)\n",
    "\n",
    "    # =================== BGL LEVEL (Blood Glucose) ===================\n",
    "    num_normal_bgl = int(total_rows * 0.80)  # 80% Normal\n",
    "    num_hypo_bgl = int(total_rows * 0.10)    # 10% Hypoglycemia\n",
    "    num_hyper_bgl = total_rows - (num_normal_bgl + num_hypo_bgl)  # 10% Hyperglycemia\n",
    "\n",
    "    # Generate BGL values\n",
    "    normal_bgl = np.random.uniform(80, 140, num_normal_bgl)\n",
    "    hypo_bgl = np.random.uniform(50, 69, num_hypo_bgl)\n",
    "    hyper_bgl = np.random.uniform(181, 250, num_hyper_bgl)\n",
    "\n",
    "    # Combine & shuffle BGL values\n",
    "    bgl_values = np.concatenate([normal_bgl, hypo_bgl, hyper_bgl])\n",
    "    np.random.shuffle(bgl_values)\n",
    "\n",
    "    # =================== ECG LEVEL (Heart Rate Condition) ===================\n",
    "    num_normal_ecg = int(total_rows * 0.80)  # 80% Normal\n",
    "    num_bradycardia = int(total_rows * 0.10)  # 10% Bradycardia (Slow HR)\n",
    "    num_tachycardia = total_rows - (num_normal_ecg + num_bradycardia)  # 10% Tachycardia (Fast HR)\n",
    "\n",
    "    # Generate ECG values (Heart Rate in beats per minute)\n",
    "    normal_ecg = np.random.uniform(60, 100, num_normal_ecg)  # Normal HR (60-100 bpm)\n",
    "    bradycardia_ecg = np.random.uniform(30, 59, num_bradycardia)  # Bradycardia (<60 bpm)\n",
    "    tachycardia_ecg = np.random.uniform(101, 180, num_tachycardia)  # Tachycardia (>100 bpm)\n",
    "\n",
    "    # Combine & shuffle ECG values\n",
    "    ecg_values = np.concatenate([normal_ecg, bradycardia_ecg, tachycardia_ecg])\n",
    "    np.random.shuffle(ecg_values)\n",
    "\n",
    "    # =================== Add to DataFrame ===================\n",
    "    df[\"BGL Level\"] = bgl_values\n",
    "    df[\"ECG Level\"] = ecg_values\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b25800f6-0f19-4779-96a0-38ae95f4b9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num.</th>\n",
       "      <th>subject_ID</th>\n",
       "      <th>Sex(M/F)</th>\n",
       "      <th>Age(year)</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Systolic Blood Pressure(mmHg)</th>\n",
       "      <th>Diastolic Blood Pressure(mmHg)</th>\n",
       "      <th>Heart Rate(b/m)</th>\n",
       "      <th>BMI(kg/m^2)</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>cerebral infarction</th>\n",
       "      <th>cerebrovascular disease</th>\n",
       "      <th>BGL Level</th>\n",
       "      <th>ECG Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>152</td>\n",
       "      <td>63</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>97</td>\n",
       "      <td>27.268006</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.859550</td>\n",
       "      <td>88.765868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>157</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>20.284799</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.434692</td>\n",
       "      <td>48.573841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>150</td>\n",
       "      <td>47</td>\n",
       "      <td>101</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>20.888889</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.822633</td>\n",
       "      <td>82.923029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>172</td>\n",
       "      <td>65</td>\n",
       "      <td>136</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>21.971336</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.492669</td>\n",
       "      <td>93.883911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>155</td>\n",
       "      <td>65</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>27.055151</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.915657</td>\n",
       "      <td>66.255639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 Num. subject_ID Sex(M/F) Age(year) Height(cm) Weight(kg)  \\\n",
       "0    1          2   Female        45        152         63   \n",
       "1    2          3   Female        50        157         50   \n",
       "2    3          6   Female        47        150         47   \n",
       "3    4          8     Male        45        172         65   \n",
       "4    5          9   Female        46        155         65   \n",
       "\n",
       "0 Systolic Blood Pressure(mmHg) Diastolic Blood Pressure(mmHg)  \\\n",
       "0                           161                             89   \n",
       "1                           160                             93   \n",
       "2                           101                             71   \n",
       "3                           136                             93   \n",
       "4                           123                             73   \n",
       "\n",
       "0 Heart Rate(b/m) BMI(kg/m^2)          Hypertension Diabetes  \\\n",
       "0              97   27.268006  Stage 2 hypertension      NaN   \n",
       "1              76   20.284799  Stage 2 hypertension      NaN   \n",
       "2              79   20.888889                Normal      NaN   \n",
       "3              87   21.971336       Prehypertension      NaN   \n",
       "4              73   27.055151       Prehypertension      NaN   \n",
       "\n",
       "0 cerebral infarction cerebrovascular disease   BGL Level  ECG Level  \n",
       "0                 NaN                     NaN   56.859550  88.765868  \n",
       "1                 NaN                     NaN  129.434692  48.573841  \n",
       "2                 NaN                     NaN  116.822633  82.923029  \n",
       "3                 NaN                     NaN  101.492669  93.883911  \n",
       "4                 NaN                     NaN   88.915657  66.255639  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=add_bgl_ecg_columns(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "523141ba-fb1f-46a8-9ffb-5b85eb8e575c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sex // apply one hot or label idk which\n",
    "#noomralize the age,height,weight,heartrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "84899b86-476d-436d-90e1-b68077d544a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_health_data(df):\n",
    "    selected_columns = [2,3,4,5,8,10,15,6,7,14]  # Column indices\n",
    "    df_selected = df.iloc[:, selected_columns].copy()  # Select columns correctly\n",
    "    # Label Encoding for Sex (Male = 1, Female = 0)\n",
    "    df_selected[\"Sex(M/F)\"] = LabelEncoder().fit_transform(df_selected[\"Sex(M/F)\"])\n",
    "    df_selected[\"Hypertension\"] = LabelEncoder().fit_transform(df_selected[\"Hypertension\"])\n",
    "\n",
    "    # Convert numerical columns to numeric type and nomralize them\n",
    "    numeric_cols = [\n",
    "        \"Age(year)\", \n",
    "        \"Height(cm)\", \n",
    "        \"Weight(kg)\", \n",
    "        \"Heart Rate(b/m)\", \n",
    "        \"Systolic Blood Pressure(mmHg)\", \n",
    "        \"Diastolic Blood Pressure(mmHg)\",\n",
    "        \"BGL Level\",\n",
    "        \"ECG Level\",\n",
    "    ]\n",
    "    df_selected[numeric_cols] = df_selected[numeric_cols].apply(pd.to_numeric, errors='coerce')\n",
    "    scaler = MinMaxScaler()\n",
    "    df_selected[numeric_cols] = scaler.fit_transform(df_selected[numeric_cols])\n",
    "    return df_selected,scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5fbeb34a-3d63-4b0b-a0e9-72f04dea71bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_req,scaler_for_df=preprocess_health_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "98113e36-f4b3-446e-b527-469018c147d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex(M/F)</th>\n",
       "      <th>Age(year)</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Heart Rate(b/m)</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>ECG Level</th>\n",
       "      <th>Systolic Blood Pressure(mmHg)</th>\n",
       "      <th>Diastolic Blood Pressure(mmHg)</th>\n",
       "      <th>BGL Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.208955</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>3</td>\n",
       "      <td>0.124870</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>0.408521</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.098039</td>\n",
       "      <td>0.164179</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370474</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.446154</td>\n",
       "      <td>0.343650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.369231</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.432836</td>\n",
       "      <td>0.648148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.448847</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>0.264799</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.432836</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>1</td>\n",
       "      <td>0.251299</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.200109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>1</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0</td>\n",
       "      <td>0.390304</td>\n",
       "      <td>0.303922</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.345897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.215686</td>\n",
       "      <td>0.164179</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.332207</td>\n",
       "      <td>0.127451</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.397270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>1</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.607843</td>\n",
       "      <td>0.283582</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.415385</td>\n",
       "      <td>0.203598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.402985</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0</td>\n",
       "      <td>0.446766</td>\n",
       "      <td>0.254902</td>\n",
       "      <td>0.415385</td>\n",
       "      <td>0.396267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.328358</td>\n",
       "      <td>0.240741</td>\n",
       "      <td>0</td>\n",
       "      <td>0.528513</td>\n",
       "      <td>0.274510</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0    Sex(M/F)  Age(year)  Height(cm)  Weight(kg)  Heart Rate(b/m)  \\\n",
       "0           0   0.369231    0.137255    0.402985         0.833333   \n",
       "1           0   0.446154    0.235294    0.208955         0.444444   \n",
       "2           0   0.400000    0.098039    0.164179         0.500000   \n",
       "3           1   0.369231    0.529412    0.432836         0.648148   \n",
       "4           0   0.384615    0.196078    0.432836         0.388889   \n",
       "..        ...        ...         ...         ...              ...   \n",
       "214         1   0.046154    0.686275    0.507463         0.462963   \n",
       "215         0   0.061538    0.215686    0.164179         0.500000   \n",
       "216         1   0.061538    0.607843    0.283582         0.370370   \n",
       "217         1   0.061538    0.549020    0.402985         0.277778   \n",
       "218         1   0.046154    0.588235    0.328358         0.240741   \n",
       "\n",
       "0    Hypertension  ECG Level  Systolic Blood Pressure(mmHg)  \\\n",
       "0               3   0.412252                       0.794118   \n",
       "1               3   0.124870                       0.784314   \n",
       "2               0   0.370474                       0.205882   \n",
       "3               1   0.448847                       0.549020   \n",
       "4               1   0.251299                       0.421569   \n",
       "..            ...        ...                            ...   \n",
       "214             0   0.390304                       0.303922   \n",
       "215             0   0.332207                       0.127451   \n",
       "216             1   1.000000                       0.392157   \n",
       "217             0   0.446766                       0.254902   \n",
       "218             0   0.528513                       0.274510   \n",
       "\n",
       "0    Diastolic Blood Pressure(mmHg)  BGL Level  \n",
       "0                          0.723077   0.035226  \n",
       "1                          0.784615   0.408521  \n",
       "2                          0.446154   0.343650  \n",
       "3                          0.784615   0.264799  \n",
       "4                          0.476923   0.200109  \n",
       "..                              ...        ...  \n",
       "214                        0.430769   0.345897  \n",
       "215                        0.230769   0.397270  \n",
       "216                        0.415385   0.203598  \n",
       "217                        0.415385   0.396267  \n",
       "218                        0.400000   0.000000  \n",
       "\n",
       "[219 rows x 10 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_req"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "98d66086-6214-4930-96a3-b68e83ba48b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5248927a-4d98-4ba3-8711-7e617b02434c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def prepare_data(ppg_data, df_static, df_targets, test_size=0.3, val_size=0.5, random_state=42):\n",
    "    # Convert to NumPy arrays and ensure float32\n",
    "    X_ppg = np.array(ppg_data, dtype=np.float32)\n",
    "    X_static = df_static.to_numpy().astype(np.float32)\n",
    "    y = df_targets.to_numpy().astype(np.float32)\n",
    "\n",
    "    # Handle NaNs (replace with 0)\n",
    "    X_ppg = np.nan_to_num(X_ppg, nan=0.0)\n",
    "    X_static = np.nan_to_num(X_static, nan=0.0)\n",
    "    y = np.nan_to_num(y, nan=0.0)\n",
    "\n",
    "    # Validate and fix PPG shape (ensure 3D: [samples, timesteps, 1])\n",
    "    if len(X_ppg.shape) == 2:  # If 2D (e.g., [219, 6300])\n",
    "        X_ppg = X_ppg[..., np.newaxis]  # Add channel dimension: [219, 6300, 1]\n",
    "    elif len(X_ppg.shape) != 3 or X_ppg.shape[2] != 1:\n",
    "        raise ValueError(f\"PPG data must be 3D with shape (samples, timesteps, 1), got {X_ppg.shape}\")\n",
    "\n",
    "    # Validate static and target shapes\n",
    "    if len(X_static.shape) != 2 or X_static.shape[1] != df_static.shape[1]:\n",
    "        raise ValueError(f\"Static data must be 2D with {df_static.shape[1]} features, got {X_static.shape}\")\n",
    "    if len(y.shape) != 2 or y.shape[1] != df_targets.shape[1]:\n",
    "        raise ValueError(f\"Target data must be 2D with {df_targets.shape[1]} targets, got {y.shape}\")\n",
    "\n",
    "    # Ensure consistent number of samples\n",
    "    n_samples = X_ppg.shape[0]\n",
    "    if X_static.shape[0] != n_samples or y.shape[0] != n_samples:\n",
    "        raise ValueError(f\"All inputs must have same number of samples ({n_samples}), got PPG={X_ppg.shape[0]}, Static={X_static.shape[0]}, BP={y.shape[0]}\")\n",
    "\n",
    "    # First split: Train vs Temp (val + test)\n",
    "    X_ppg_train, X_ppg_temp, X_static_train, X_static_temp, y_train, y_temp = train_test_split(\n",
    "        X_ppg, X_static, y,\n",
    "        test_size=test_size,\n",
    "        random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Second split: Temp into Val and Test\n",
    "    X_ppg_val, X_ppg_test, X_static_val, X_static_test, y_val, y_test = train_test_split(\n",
    "        X_ppg_temp, X_static_temp, y_temp,\n",
    "        test_size=val_size,\n",
    "        random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Enhanced debugging output\n",
    "    print(\"Data Preparation Details:\")\n",
    "    print(f\"Input PPG: shape={X_ppg.shape}, dtype={X_ppg.dtype}, sample={X_ppg[0, :5, 0]}\")\n",
    "    print(f\"Input Static: shape={X_static.shape}, dtype={X_static.dtype}, sample={X_static[0]}\")\n",
    "    print(f\"Input BP: shape={y.shape}, dtype={y.dtype}, sample={y[0]}\")\n",
    "    print(\"\\nSplit Shapes:\")\n",
    "    print(f\"Train: PPG={X_ppg_train.shape}, Static={X_static_train.shape}, BP={y_train.shape}\")\n",
    "    print(f\"Val: PPG={X_ppg_val.shape}, Static={X_static_val.shape}, BP={y_val.shape}\")\n",
    "    print(f\"Test: PPG={X_ppg_test.shape}, Static={X_static_test.shape}, BP={y_test.shape}\")\n",
    "\n",
    "    return (X_ppg_train, X_static_train, y_train,\n",
    "            X_ppg_val, X_static_val, y_val,\n",
    "            X_ppg_test, X_static_test, y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "86cb0e0c-5603-4560-a2da-2b3907094b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Preparation Details:\n",
      "Input PPG: shape=(219, 6300, 1), dtype=float32, sample=[0.6375     0.63522726 0.63295454 0.6306818  0.6272727 ]\n",
      "Input Static: shape=(219, 9), dtype=float32, sample=[0.         0.36923078 0.13725491 0.40298507 0.8333333  3.\n",
      " 0.4122518  0.7941176  0.72307694]\n",
      "Input BP: shape=(219, 1), dtype=float32, sample=[0.03522612]\n",
      "\n",
      "Split Shapes:\n",
      "Train: PPG=(153, 6300, 1), Static=(153, 9), BP=(153, 1)\n",
      "Val: PPG=(33, 6300, 1), Static=(33, 9), BP=(33, 1)\n",
      "Test: PPG=(33, 6300, 1), Static=(33, 9), BP=(33, 1)\n"
     ]
    }
   ],
   "source": [
    "# Usage\n",
    "X_ppg_train, X_static_train, y_train, \\\n",
    "X_ppg_val, X_static_val, y_val, \\\n",
    "X_ppg_test, X_static_test, y_test = prepare_data(\n",
    "    ppg_data=normalized_ppg_data,\n",
    "    df_static=df_req.iloc[:, :9],  # e.g., sex, age, height, weight, heart_rate ,SBP, DBP\n",
    "    df_targets=df_req.iloc[:, 9:]  # e.g.,BGL\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "90bc8e81-3b67-48e9-8ee0-078b33c516c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, Model, Input, regularizers\n",
    "\n",
    "def build_lstm_model(timesteps=6300, static_features=9):\n",
    "    \"\"\"\n",
    "    Build an LSTM-based hybrid model for blood pressure prediction.\n",
    "    \n",
    "    Parameters:\n",
    "    - timesteps: Number of PPG timesteps (default: 6300)\n",
    "    - static_features: Number of static features (default: 9)\n",
    "    \n",
    "    Returns:\n",
    "    - Compiled Keras model\n",
    "    \"\"\"\n",
    "    # Define inputs\n",
    "    ppg_input = Input(shape=(timesteps, 1), name='ppg_input')  # PPG time-series: (samples, 6300, 1)\n",
    "    static_input = Input(shape=(static_features,), name='static_input')  # Static features: (samples, 5)\n",
    "\n",
    "    # LSTM branch for PPG\n",
    "    x = layers.LSTM(64, return_sequences=False)(ppg_input)  # 64 units, outputs (samples, 64)\n",
    "    x = layers.BatchNormalization()(x)  # Normalize LSTM outputs for stability\n",
    "    ppg_features = layers.Dense(128, activation='relu', \n",
    "                               kernel_regularizer=regularizers.l2(0.01))(x)  # 128 units, L2 penalty\n",
    "\n",
    "    # Combine PPG features with static features\n",
    "    combined = layers.concatenate([ppg_features, static_input])  # (samples, 128 + 9 = 137)\n",
    "\n",
    "    # Dense layers for prediction\n",
    "    x = layers.Dense(64, activation='relu', \n",
    "                    kernel_regularizer=regularizers.l2(0.01))(combined)  # 64 units, L2 penalty\n",
    "    x = layers.BatchNormalization()(x)  # Normalize for stability\n",
    "    x = layers.Dropout(0.2)(x)  # 20% dropout to prevent overfitting\n",
    "    output = layers.Dense(1, activation='linear', name='bp_output')(x)  # 1 output:\n",
    "\n",
    "    # Define and compile model\n",
    "    model = Model(inputs=[ppg_input, static_input], outputs=output)\n",
    "    model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
    "\n",
    "    # Print model summary\n",
    "    print(\"Model Summary:\")\n",
    "    model.summary()\n",
    "\n",
    "    return model\n",
    "\n",
    "# Example usage\n",
    "# model = build_lstm_model(timesteps=6300, static_features=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5f068679-e938-4d60-817c-7f14156fbb65",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import tensorflow as tf\n",
    "\n",
    "def train_model(model, X_ppg_train, X_static_train, y_train, X_ppg_val, X_static_val, y_val,\n",
    "                epochs=50, batch_size=16):\n",
    "    # Define early stopping callback\n",
    "    early_stopping = EarlyStopping(\n",
    "        monitor='val_loss',    # Monitor validation loss\n",
    "        patience=10,           # Stop if no improvement for 10 epochs\n",
    "        restore_best_weights=True  # Revert to best weights\n",
    "    )\n",
    "\n",
    "    # Train the model\n",
    "    history = model.fit(\n",
    "        {'ppg_input': X_ppg_train, 'static_input': X_static_train},  # Dictionary of named inputs\n",
    "        y_train,\n",
    "        validation_data=({'ppg_input': X_ppg_val, 'static_input': X_static_val}, y_val),\n",
    "        epochs=epochs,\n",
    "        batch_size=batch_size,\n",
    "        callbacks=[early_stopping],\n",
    "        verbose=1  # Show progress per epoch\n",
    "    )\n",
    "\n",
    "    # Plot training history\n",
    "    plt.figure(figsize=(12, 4))\n",
    "\n",
    "    # Loss plot\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss (MSE)')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    # MAE plot\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['mae'], label='Train MAE')\n",
    "    plt.plot(history.history['val_mae'], label='Validation MAE')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('MAE')\n",
    "    plt.title('Training and Validation MAE')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "6d71de6b-790d-416c-9a2d-b5b70836ade1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Summary:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_6\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_6\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ ppg_input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ lstm_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,896</span> │ ppg_input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_12        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ lstm_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ batch_normalization_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ static_input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">137</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ static_input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,832</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_13        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ bp_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │ dropout_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ ppg_input (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6300\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ lstm_6 (\u001b[38;5;33mLSTM\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │          \u001b[38;5;34m16,896\u001b[0m │ ppg_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_12        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │             \u001b[38;5;34m256\u001b[0m │ lstm_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m8,320\u001b[0m │ batch_normalization_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ static_input (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_6 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m137\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ static_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m8,832\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_13        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │             \u001b[38;5;34m256\u001b[0m │ dense_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ batch_normalization_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ bp_output (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │              \u001b[38;5;34m65\u001b[0m │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,625</span> (135.25 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m34,625\u001b[0m (135.25 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,369</span> (134.25 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34,369\u001b[0m (134.25 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_lstm_model(timesteps=6300, static_features=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c3a1dce3-799f-49d7-a674-bc91cae62d75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_ppg_train shape: (153, 6300, 1)\n",
      "X_static_train shape: (153, 9)\n",
      "y_train shape: (153, 1)\n",
      "X_ppg_val shape: (33, 6300, 1)\n",
      "X_static_val shape: (33, 9)\n",
      "y_val shape: (33, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(\"X_ppg_train shape:\", X_ppg_train.shape)  # Should be (samples, 6300, 1)\n",
    "print(\"X_static_train shape:\", X_static_train.shape)  # Should be (samples, 9)\n",
    "print(\"y_train shape:\", y_train.shape)  # Should be (samples, 1)\n",
    "print(\"X_ppg_val shape:\", X_ppg_val.shape)  # Should be (samples, 6300, 1)\n",
    "print(\"X_static_val shape:\", X_static_val.shape)  # Should be (samples, 9)\n",
    "print(\"y_val shape:\", y_val.shape)  # Should be (samples, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c426b657-2aab-4090-8c7a-268e3543044d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 2.3845 - mae: 0.6468 - val_loss: 1.7451 - val_mae: 0.2510\n",
      "Epoch 2/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - loss: 2.1569 - mae: 0.5260 - val_loss: 1.6655 - val_mae: 0.1648\n",
      "Epoch 3/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 2.0034 - mae: 0.5200 - val_loss: 1.6149 - val_mae: 0.1445\n",
      "Epoch 4/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2s/step - loss: 1.9955 - mae: 0.5123 - val_loss: 1.5762 - val_mae: 0.1467\n",
      "Epoch 5/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 2.0782 - mae: 0.5625 - val_loss: 1.5421 - val_mae: 0.1483\n",
      "Epoch 6/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 2s/step - loss: 1.9769 - mae: 0.5360 - val_loss: 1.5096 - val_mae: 0.1446\n",
      "Epoch 7/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 1.7863 - mae: 0.4413 - val_loss: 1.4752 - val_mae: 0.1472\n",
      "Epoch 8/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 1.9048 - mae: 0.5270 - val_loss: 1.4438 - val_mae: 0.1571\n",
      "Epoch 9/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 3s/step - loss: 1.8247 - mae: 0.4963 - val_loss: 1.4128 - val_mae: 0.1547\n",
      "Epoch 10/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 1.8067 - mae: 0.5085 - val_loss: 1.3882 - val_mae: 0.1723\n",
      "Epoch 11/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 1.7309 - mae: 0.4938 - val_loss: 1.3589 - val_mae: 0.1649\n",
      "Epoch 12/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 3s/step - loss: 1.6990 - mae: 0.4828 - val_loss: 1.3304 - val_mae: 0.1495\n",
      "Epoch 13/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 3s/step - loss: 1.7386 - mae: 0.5028 - val_loss: 1.3051 - val_mae: 0.1577\n",
      "Epoch 14/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 3s/step - loss: 1.5787 - mae: 0.4388 - val_loss: 1.2795 - val_mae: 0.1551\n",
      "Epoch 15/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 3s/step - loss: 1.7246 - mae: 0.4795 - val_loss: 1.2541 - val_mae: 0.1505\n",
      "Epoch 16/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - loss: 1.6768 - mae: 0.4885 - val_loss: 1.2309 - val_mae: 0.1590\n",
      "Epoch 17/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 2s/step - loss: 1.5090 - mae: 0.4461 - val_loss: 1.2119 - val_mae: 0.1744\n",
      "Epoch 18/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 1.5832 - mae: 0.4530 - val_loss: 1.1841 - val_mae: 0.1545\n",
      "Epoch 19/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 1.4707 - mae: 0.4359 - val_loss: 1.1609 - val_mae: 0.1459\n",
      "Epoch 20/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - loss: 1.4107 - mae: 0.4197 - val_loss: 1.1406 - val_mae: 0.1542\n",
      "Epoch 21/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 1.3872 - mae: 0.3894 - val_loss: 1.1227 - val_mae: 0.1768\n",
      "Epoch 22/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 3s/step - loss: 1.3136 - mae: 0.3756 - val_loss: 1.1009 - val_mae: 0.1761\n",
      "Epoch 23/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2s/step - loss: 1.3941 - mae: 0.4068 - val_loss: 1.0804 - val_mae: 0.1781\n",
      "Epoch 24/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 1.4107 - mae: 0.4262 - val_loss: 1.0579 - val_mae: 0.1777\n",
      "Epoch 25/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 3s/step - loss: 1.3298 - mae: 0.4135 - val_loss: 1.0325 - val_mae: 0.1623\n",
      "Epoch 26/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 2s/step - loss: 1.2772 - mae: 0.4282 - val_loss: 1.0138 - val_mae: 0.1625\n",
      "Epoch 27/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 3s/step - loss: 1.2066 - mae: 0.3587 - val_loss: 0.9945 - val_mae: 0.1556\n",
      "Epoch 28/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 2s/step - loss: 1.1725 - mae: 0.3658 - val_loss: 0.9761 - val_mae: 0.1569\n",
      "Epoch 29/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 1.1732 - mae: 0.3841 - val_loss: 0.9573 - val_mae: 0.1521\n",
      "Epoch 30/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 2s/step - loss: 1.3269 - mae: 0.3888 - val_loss: 0.9397 - val_mae: 0.1619\n",
      "Epoch 31/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 3s/step - loss: 1.1931 - mae: 0.4101 - val_loss: 0.9205 - val_mae: 0.1603\n",
      "Epoch 32/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 1.1551 - mae: 0.3787 - val_loss: 0.9028 - val_mae: 0.1695\n",
      "Epoch 33/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2s/step - loss: 1.0103 - mae: 0.3061 - val_loss: 0.8856 - val_mae: 0.1672\n",
      "Epoch 34/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 3s/step - loss: 1.0145 - mae: 0.3084 - val_loss: 0.8698 - val_mae: 0.1575\n",
      "Epoch 35/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 3s/step - loss: 1.0480 - mae: 0.3754 - val_loss: 0.8523 - val_mae: 0.1565\n",
      "Epoch 36/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2s/step - loss: 1.0159 - mae: 0.3571 - val_loss: 0.8366 - val_mae: 0.1631\n",
      "Epoch 37/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 3s/step - loss: 0.9942 - mae: 0.3254 - val_loss: 0.8191 - val_mae: 0.1535\n",
      "Epoch 38/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - loss: 0.9902 - mae: 0.3397 - val_loss: 0.8010 - val_mae: 0.1592\n",
      "Epoch 39/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 1.0056 - mae: 0.3490 - val_loss: 0.7836 - val_mae: 0.1585\n",
      "Epoch 40/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 3s/step - loss: 0.9357 - mae: 0.3428 - val_loss: 0.7701 - val_mae: 0.1744\n",
      "Epoch 41/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 0.8859 - mae: 0.2959 - val_loss: 0.7615 - val_mae: 0.1891\n",
      "Epoch 42/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 2s/step - loss: 0.8317 - mae: 0.2708 - val_loss: 0.7419 - val_mae: 0.1748\n",
      "Epoch 43/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 0.8750 - mae: 0.3383 - val_loss: 0.7245 - val_mae: 0.1605\n",
      "Epoch 44/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 0.8626 - mae: 0.3095 - val_loss: 0.7167 - val_mae: 0.1848\n",
      "Epoch 45/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 3s/step - loss: 0.8496 - mae: 0.3249 - val_loss: 0.7059 - val_mae: 0.1939\n",
      "Epoch 46/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step - loss: 0.8361 - mae: 0.3274 - val_loss: 0.6892 - val_mae: 0.1829\n",
      "Epoch 47/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 3s/step - loss: 0.7671 - mae: 0.2715 - val_loss: 0.6845 - val_mae: 0.2075\n",
      "Epoch 48/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2s/step - loss: 0.7554 - mae: 0.2747 - val_loss: 0.6697 - val_mae: 0.2047\n",
      "Epoch 49/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 2s/step - loss: 0.8344 - mae: 0.3372 - val_loss: 0.6539 - val_mae: 0.1994\n",
      "Epoch 50/50\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - loss: 0.7474 - mae: 0.2754 - val_loss: 0.6423 - val_mae: 0.1954\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    {\"ppg_input\": X_ppg_train, \"static_input\": X_static_train},  # Correct dictionary format\n",
    "    y_train,\n",
    "    validation_data=({\"ppg_input\": X_ppg_val, \"static_input\": X_static_val}, y_val),  # Correct validation data format\n",
    "    epochs=50,\n",
    "    batch_size=16\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c88b29ab-a583-4767-8396-f6274dca97f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model.save(\"lstm_bp_model.h5\")  # Saves as HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "c41cbebb-6197-48d1-aeeb-7f45b6a2d310",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_6\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_6\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ ppg_input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ lstm_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,896</span> │ ppg_input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_12        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ lstm_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ batch_normalization_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ static_input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">137</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ static_input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,832</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_13        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ bp_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │ dropout_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ ppg_input (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6300\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ lstm_6 (\u001b[38;5;33mLSTM\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │          \u001b[38;5;34m16,896\u001b[0m │ ppg_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_12        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │             \u001b[38;5;34m256\u001b[0m │ lstm_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m8,320\u001b[0m │ batch_normalization_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ static_input (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_6 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m137\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ static_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m8,832\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ batch_normalization_13        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │             \u001b[38;5;34m256\u001b[0m │ dense_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ batch_normalization_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ bp_output (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │              \u001b[38;5;34m65\u001b[0m │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">103,365</span> (403.77 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m103,365\u001b[0m (403.77 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,369</span> (134.25 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34,369\u001b[0m (134.25 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">68,740</span> (268.52 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m68,740\u001b[0m (268.52 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "cf602594-5b1f-463c-b8bc-68b8ec4476cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_single_signal(signal):\n",
    "    signal=np.array(signal)\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    normalized_signal = scaler.fit_transform(signal.reshape(-1, 1)).flatten()\n",
    "    return normalized_signal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "01357ea1-38fa-48ad-9fe7-7244470d8eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Min Values: [ 21.         145.          36.          52.          80.\n",
      "  42.          50.01096599  31.11004591]\n",
      "Feature Max Values: [ 86.         196.         103.         106.         182.\n",
      " 107.         244.42874032 170.96588475]\n",
      "Feature Scale Factors: [0.01538462 0.01960784 0.01492537 0.01851852 0.00980392 0.01538462\n",
      " 0.00514356 0.00715022]\n",
      "Feature Offsets: [-0.32307692 -2.84313725 -0.53731343 -0.96296296 -0.78431373 -0.64615385\n",
      " -0.25723454 -0.22244367]\n"
     ]
    }
   ],
   "source": [
    "# Get min, max, and scale values\n",
    "print(\"Feature Min Values:\", scaler_for_df.data_min_)\n",
    "print(\"Feature Max Values:\", scaler_for_df.data_max_)\n",
    "print(\"Feature Scale Factors:\", scaler_for_df.scale_)\n",
    "print(\"Feature Offsets:\", scaler_for_df.min_)  # Used internally for scaling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "0101e373-12f1-4f4f-8a6f-d81e622e3a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_value(value, index, scaler):\n",
    "    \"\"\"Scales a single value using a pre-fitted MinMaxScaler.\"\"\"\n",
    "    return (value - scaler.data_min_[index]) * scaler.scale_[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "94b7208d-4c39-48f0-a35c-6feda073a9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_static(static,scaler):\n",
    "    static = np.array(static, dtype=np.float32)  # Ensure NumPy array\n",
    "    scaled_static = static\n",
    "    index_list=[[1,0],[2,1],[3,2],[4,3],[6,4],[7,5],[8,6]]\n",
    "    for i,j in index_list:\n",
    "        scaled_static[i]=normalize_value(static[i],j,scaler)\n",
    "    return scaled_static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "bd2acf94-8f34-48c5-812c-e14ab3f038b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44999999999999996"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_single_signal(ppg_data[0])[6299]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "01df4b12-80f3-4bf7-bc34-028a79b49e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44999999999999996"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_ppg_data[0][6299]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "6e51a6b8-759d-48de-9a99-8ea58bfb033f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_bgl(signal, static, model, scaler_for_df):\n",
    "    normalized_signal = normalize_single_signal(signal)  # Normalize PPG signal\n",
    "    normalized_static = normalize_static(static, scaler_for_df)\n",
    "    normalized_signal = signal.reshape(1, 6300, 1)  # Shape (1, 6300, 1)\n",
    "    normalized_static = static.reshape(1, -1)  # Shape (1, num_static_features)\n",
    "    predicted_bgl = model.predict({\"ppg_input\": normalized_signal, \"static_input\": normalized_static})\n",
    "    return predicted_bgl[0][0] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "9e1ef768-a027-4185-bf65-694a0d039cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step\n",
      "0.5140327\n",
      "[0.1917]\n"
     ]
    }
   ],
   "source": [
    "print(predict_bgl(X_ppg_train[0],X_static_train[0],model,scaler_for_df))\n",
    "print(y_val[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ff115f63-78c8-49b3-95f6-ecb953f0f9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_for_df.data_max_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "4a84bfb3-fb41-4c8a-9347-18b767543023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step\n",
      "0 [0.15597202] [0.4413448]\n",
      "1 [0.28513178] [0.40964374]\n",
      "2 [0.3973067] [0.59718496]\n",
      "3 [0.4230558] [0.4035743]\n",
      "4 [0.37565535] [0.39182305]\n",
      "5 [0.334657] [0.57810926]\n",
      "6 [0.04841661] [0.55011237]\n",
      "7 [0.45212075] [0.55265725]\n",
      "8 [0.44393235] [0.61767966]\n",
      "9 [0.34626135] [0.50347626]\n",
      "10 [0.38175657] [0.5804067]\n",
      "11 [0.2905726] [0.5009359]\n",
      "12 [0.08823036] [0.52042764]\n",
      "13 [0.3288136] [0.36532807]\n",
      "14 [0.1886792] [0.43709916]\n",
      "15 [0.27078998] [0.5343541]\n",
      "16 [0.3962674] [0.32538307]\n",
      "17 [0.2811834] [0.39458802]\n",
      "18 [0.36239684] [0.44191548]\n",
      "19 [0.34097245] [0.40195984]\n",
      "20 [0.34546962] [0.45223433]\n",
      "21 [0.2670726] [0.43408644]\n",
      "22 [0.20293666] [0.5960347]\n",
      "23 [0.0826071] [0.49458188]\n",
      "24 [0.26986217] [0.4156874]\n",
      "25 [0.31692764] [0.44381195]\n",
      "26 [0.27019456] [0.45171985]\n",
      "27 [0.1994752] [0.44154423]\n",
      "28 [0.00960518] [0.42709392]\n",
      "29 [0.43790913] [0.3977991]\n",
      "30 [0.21606465] [0.45926622]\n",
      "31 [0.23551995] [0.43894282]\n",
      "32 [0.17534031] [0.43150637]\n",
      "MAE: 0.1961\n",
      "MSE: 0.0539\n",
      "RMSE: 0.2322\n",
      "R² Score: -3.1292\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "# Predict on test set\n",
    "y_pred = model.predict({\"ppg_input\": X_ppg_test, \"static_input\": X_static_test})\n",
    "# Compute metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)  # Root Mean Squared Error\n",
    "r2 = r2_score(y_test, y_pred)  # R² Score\n",
    "for i in range(y_test.size):\n",
    "    print(i,y_test[i],y_pred[i])\n",
    "# Print results\n",
    "print(f\"MAE: {mae:.4f}\")\n",
    "print(f\"MSE: {mse:.4f}\")\n",
    "print(f\"RMSE: {rmse:.4f}\")\n",
    "print(f\"R² Score: {r2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "6bac5bf7-ed22-4aa3-9c24-37447a4fc10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num.</th>\n",
       "      <th>subject_ID</th>\n",
       "      <th>Sex(M/F)</th>\n",
       "      <th>Age(year)</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Systolic Blood Pressure(mmHg)</th>\n",
       "      <th>Diastolic Blood Pressure(mmHg)</th>\n",
       "      <th>Heart Rate(b/m)</th>\n",
       "      <th>BMI(kg/m^2)</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>cerebral infarction</th>\n",
       "      <th>cerebrovascular disease</th>\n",
       "      <th>BGL Level</th>\n",
       "      <th>ECG Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>152</td>\n",
       "      <td>63</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>97</td>\n",
       "      <td>27.268006</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.859550</td>\n",
       "      <td>88.765868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>157</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>20.284799</td>\n",
       "      <td>Stage 2 hypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.434692</td>\n",
       "      <td>48.573841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>150</td>\n",
       "      <td>47</td>\n",
       "      <td>101</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>20.888889</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.822633</td>\n",
       "      <td>82.923029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>172</td>\n",
       "      <td>65</td>\n",
       "      <td>136</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>21.971336</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.492669</td>\n",
       "      <td>93.883911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>155</td>\n",
       "      <td>65</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>27.055151</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.915657</td>\n",
       "      <td>66.255639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 Num. subject_ID Sex(M/F) Age(year) Height(cm) Weight(kg)  \\\n",
       "0    1          2   Female        45        152         63   \n",
       "1    2          3   Female        50        157         50   \n",
       "2    3          6   Female        47        150         47   \n",
       "3    4          8     Male        45        172         65   \n",
       "4    5          9   Female        46        155         65   \n",
       "\n",
       "0 Systolic Blood Pressure(mmHg) Diastolic Blood Pressure(mmHg)  \\\n",
       "0                           161                             89   \n",
       "1                           160                             93   \n",
       "2                           101                             71   \n",
       "3                           136                             93   \n",
       "4                           123                             73   \n",
       "\n",
       "0 Heart Rate(b/m) BMI(kg/m^2)          Hypertension Diabetes  \\\n",
       "0              97   27.268006  Stage 2 hypertension      NaN   \n",
       "1              76   20.284799  Stage 2 hypertension      NaN   \n",
       "2              79   20.888889                Normal      NaN   \n",
       "3              87   21.971336       Prehypertension      NaN   \n",
       "4              73   27.055151       Prehypertension      NaN   \n",
       "\n",
       "0 cerebral infarction cerebrovascular disease   BGL Level  ECG Level  \n",
       "0                 NaN                     NaN   56.859550  88.765868  \n",
       "1                 NaN                     NaN  129.434692  48.573841  \n",
       "2                 NaN                     NaN  116.822633  82.923029  \n",
       "3                 NaN                     NaN  101.492669  93.883911  \n",
       "4                 NaN                     NaN   88.915657  66.255639  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "077ad653-0f74-41a4-9c87-1d24c8c3fda2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=get_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "54a2e307-d709-4add-94af-ad7330485476",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppg_signal_with_noise_segs=load_ppg_signals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72e54ace-02aa-48f2-81b8-a0a67ccb48c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wmUwoU6YMWrRoYTVh5+TJkxETE4PPP/8cc+fOxb333ovPPvsM48aNQ9GiRb2WdlfSpFZ0dDS++uorpKSkICkpCbm5uZgwYQKef/55VK1aFZ9++inOnDkDg8GAKlWqYNq0aRgxYoTDbUZFRWHt2rV49dVXMWLECNy+fRv169fH77//bndB5ObNmzAYDEhMTHSa1gkTJuRfbAHEYLn53JAG4c0TscrdcSC1devW/LHbu3XrZvf+3Llz0b9/f6fpIiIiIiJ9MwiOWiNERERERKSJEydO4N5778WECRPw+uuva50c3XrggQdQsWJFLFmyxGvbfPXVV/Htt9/i6NGjVvMHEBEREVFoYhCdiIiIiEhje/fuxbfffovmzZujSJEiOHLkCKZOnYrMzEwcOHAApUqV0jqJupSZmYmSJUtiz549qFmzpte227hxYwwePBhDhgzx2jaJiIiIKHAxiE5EREREpLFjx47hf//7H/bu3Yvr168jLi4Obdq0weTJk1GjRg2tk0dEREREFNIYRCciIiIiIiIiIiIiUhCmdQKIiIiIiIiIiIiIiPSKQXQiIiIiIiIiIiIiIgUMohMRERERERERERERKWAQnYiIiIiIiIiIiIhIAYPoREREREREREREREQKGEQnIiIiIiIiIiIiIlLAIDoRERERERERERERkQIG0YmIiIiIiIiIiIiIFDCITkRERERERERERESkgEF0IiIiIiIiIiIiIiIFDKITERERERERERERESlgEJ2IiIiIiIiIiIiISAGD6EREREREREREREREChhEJyIiIiIiIiIiIiJSwCA6EREREREREREREZECBtGJiIiIiIiIiIiIiBQwiE5EREREREREREREpIBBdCIiIiIiIiIiIiIiBQyiExEREREREREREREpYBCdiIiIiIiIiIiIiEgBg+hERERERERERERERAoYRCciIiIiIiIiIiIiUsAgOhERERERERERERGRAgbRiYiIiIiIiIiIiIgUMIhORERERERERERERKSAQXQiIiIiIiIiIiIiIgUMohMRERERERERERERKWAQnYiIiIiIiIiIiIhIAYPoREREREREREREREQKGEQnIiIiIiIiIiIiIlLAIDoRERERERERERERkQIG0YmIiIiIiIiIiIiIFDCITkRERERERERERESkgEF0IiIiIiIiIiIiIiIFDKITERERERERERERESlgEJ2IiIiIiIiIiIiISAGD6EREREREREREREREChhEJyIiIiIiIiIiIiJSwCA6EREREREREREREZECBtGJiIiIiIiIiIiIiBQwiE5EREREREREREREpIBBdCIiIiIiIiIiIiIiBQyiExEREREREREREREpYBCdiIiIiIiIiIiIiEgBg+hERERERERERERERAoYRCciIiIiIiIiIiIiUsAgOhERERERERERERGRAgbRiYiIiIiIiIiIiIgUMIhORERERERERERERKSAQXQictv27dvRs2dPVKhQAVFRUShVqhSaNWuGl19+Weuk+d3t27cxceJEbNiwwaXPffzxx7j33nsRFRWFypUrIyUlBbm5ub5JJBER6RrLVQt3ytUZM2agV69eqFy5MgwGA9q0aeOz9BERkf6xXLVwtVz9999/MXr0aDRq1AhFixZF8eLF0aJFCyxdutS3CSXSMQbRicgtv/32G5o3b47MzExMnToVq1atwkcffYQWLVrgu+++0zp5fnf79m2kpKS41NifPHkyXnjhBfTq1QsrV67E888/j3feeQfDhg3zXUKJiEiXWK5ac6dc/fzzz3Hq1Cm0a9cOJUuW9F3iiIhI91iuWnO1XF21ahV+++03PPLII1iyZAkWLlyIatWq4bHHHsOkSZN8m1ginTIIgiBonQgiCjytW7fGuXPncPjwYURERFi9ZzKZEBYWWtfoLl++jJIlS2LChAmYOHGi0/WvXLmCcuXKoV+/fpg1a1b+8nfeeQfjx4/HgQMHUKtWLR+mmIiI9ITlqjVXy1XA+nuqU6cO4uPjXb5DjIiIggPLVWuulquXL19GiRIlYDAYrJZ37doV69evx9WrVxEVFeWj1BLpU2jlGkTkNVeuXEF8fLxdhQSAbIXku+++Q7NmzRATE4PChQujU6dO2L17t916s2fPRvXq1REVFYVatWph0aJF6N+/PypVqpS/zsmTJ2EwGPD+++/jvffeQ6VKlVCoUCG0adMG//77L3Jzc/Haa6+hTJkyiIuLQ8+ePXHx4kW30tS/f38ULlwYx44dQ3JyMgoXLozy5cvj5ZdfRnZ2dn56zD3eUlJSYDAYYDAY0L9/f8Xvb8WKFcjKysKzzz5rtfzZZ5+FIAj46aefFD9LRETBh+WqZ+Wq0vdEREShieWqZ+VqfHy8XQAdAB544AHcvn0bV69eVfwsUbBiTZOI3NKsWTNs374dI0eOxPbt2x2O4/3OO+/gySefRK1atfD9999jwYIFuHHjBlq2bIlDhw7lr/fFF19gyJAhuO+++7Bs2TKMHz/e4S1nn3zyCf7880988skn+PLLL3H48GF069YNAwcOxKVLl/DVV19h6tSpWLNmDQYNGuRWmgAgNzcX3bt3R/v27fHzzz9jwIABmD59Ot577z0AQOnSpbFixQoAwMCBA7F161Zs3boVb7zxhuJ3cuDAAQBA3bp1rZaXLl0a8fHx+e8TEVFoYLnqWblKREQkxXLVN+Xq+vXrUbJkSSQkJLj8WaKAJxARueHy5cvCgw8+KAAQAAiRkZFC8+bNhSlTpgg3btzIX+/06dNCRESEMGLECKvP37hxQ0hMTBR69+4tCIIgGI1GITExUWjSpInVeqdOnRIiIyOFihUr5i87ceKEAECoV6+eYDQa85fPmDFDACB0797dahsvvviiAEDIyMhwKU2CIAjPPPOMAED4/vvvrdZNTk4WatSokf/60qVLAgBhwoQJzr46QRAEYfDgwUJUVJTse9WrVxeSkpJUbYeIiIIDy1XPylVbtWvXFlq3bu3WZ4mIKPCxXPVuuSoIgjB79mwBgPDRRx+5vQ2iQMae6ETklhIlSuCPP/7Azp078e677+Lhhx/Gv//+i7Fjx6Ju3bq4fPkyAGDlypXIy8tDv379kJeXl/9XsGBBtG7dOv+q/ZEjR5Ceno7evXtb7adChQpo0aKFbBqSk5OtbsWrWbMmAKBLly5W65mXnz592qU0mRkMBnTr1s1q2X333YdTp0658I3Zk7s9Ts17REQUfFiuel6uEhERmbFc9W65+vvvv2PYsGF49NFHMWLECK9tlyiQ2A8ORUTkgvvvvx/3338/APE2sjFjxmD69OmYOnUqpk6digsXLgAAGjduLPt5c6XiypUrAIBSpUrZrVOqVCmcOHHCbnnx4sWtXhcoUMDh8qysLABQnSaz6OhoFCxY0GpZVFRU/vbcUaJECWRlZeH27duIjo62eu/q1ato1KiR29smIqLAxXKViIjIe1iuem7lypXo1asXOnbsiIULF7LDF4UsBtGJyGsiIyMxYcIETJ8+PX9M7/j4eADA0qVLUbFiRcXPlihRAoClwiCVnp7u1XSqTZMvmcdC379/P5o0aZK/PD09HZcvX0adOnU0SRcREekHy1UiIiLvYbnqupUrV6JHjx5o3bo1fvjhh/yAP1EoYhCdiNySlpaG0qVL2y3/559/AABlypQBAHTq1AkRERE4fvw4HnnkEcXt1ahRA4mJifj+++8xatSo/OWnT5/Gli1b8rfnDWrT5IqoqCgAwJ07d1St37lzZxQsWBDz5s2zCqLPmzcPBoMBPXr08Eq6iIgoMLBcteZquUpERCTFctWaO+XqqlWr0KNHDzz44IP46aef8rdBFKoYRCcit3Tq1AnlypVDt27dcO+998JkMmHPnj2YNm0aChcujBdeeAEAUKlSJUyaNAnjxo3Df//9h86dO6NYsWK4cOECduzYgZiYGKSkpCAsLAwpKSkYOnQoHn30UQwYMADXr19HSkoKSpcubXfLmifUpskVsbGxqFixIn7++We0b98exYsXR3x8PCpVqiS7fvHixTF+/Hi88cYbKF68OJKSkrBz505MnDgRgwYNQq1atbzwnxIRUaBguWrN1XIVAP766y+cPHkSAJCZmQlBELB06VIA4i3xWvfmIyIi/2G5as3VcnXz5s3o0aMHEhMT8frrr2PPnj1W79eqVQtFihRx8z8kCkwMohORW8aPH4+ff/4Z06dPR1paGrKzs1G6dGl06NABY8eOzZ8cBQDGjh2LWrVq4aOPPsK3336L7OxsJCYmonHjxvjf//6Xv96QIUNgMBgwdepU9OzZE5UqVcJrr72Gn3/+OX+SFW9RmyZXzJkzB6+88gq6d++O7OxsPPPMM5g3b57i+uPGjUNsbCw++eQTfPDBB0hMTMRrr72GcePGuflfERFRoGK5as/VcnXmzJmYP3++1bLHHnsMADB37lz079/frXQQEVHgYblqz5Vydc2aNbhz5w5OnjyJdu3a2b2/fv16tGnTxq10EAUqgyAIgtaJICJScv36dVSvXh09evTAF198oXVyiIiIAhrLVSIiIu9huUoUOtgTnYh0Iz09HZMnT0bbtm1RokQJnDp1CtOnT8eNGzfyb7cjIiIidViuEhEReQ/LVaLQxiA6EelGVFQUTp48ieeffx5Xr15FdHQ0mjZtis8//xy1a9fWOnlEREQBheUqERGR97BcJQptHM6FiIiIiIiIiIiIiEiB96YPJiIiIiIiIiIiIiIKMgyiExEREREREREREREpYBCdiIiIiIiIiIiIiEgBJxb1E5PJhPPnzyM2NhYGg0Hr5BAREfmVIAi4ceMGypQpg7Awz6/hs1wlIqJQxTKViIjIe9SWqwyi+8n58+dRvnx5rZNBRESkqTNnzqBcuXIeb4flKhERhTqWqURERN7jrFxlEN1PYmNjAYgHpEiRIh5vLzc3F6tWrUJSUhIiIyM93h65h8dBezwG+sDjoD29H4PMzEyUL18+vzz0FMvV4MNjoA88DtrjMdAHPR8HvZepgL6/v1DBY6A9HgN94HHQnt6PgdpylUF0PzHfFlekSBGvNfajo6NRpEgRXZ6AoYLHQXs8BvrA46C9QDkG3rpNnOVq8OEx0AceB+3xGOhDIBwHvZapQGB8f8GOx0B7PAb6wOOgvUA5Bs7KVU4sSkRERERERERERESkgEF0IiIiIiIiIiIiIiIFDKITERERERERERERESngmOg6YzQakZub63S93NxcREREICsrC0aj0Q8pIznuHofIyEiEh4f7MGVEREREFIzUthfIM1q3twoUKICwMPZ5IyLyJZap/qF1meqtGByD6DohCALS09Nx/fp11esnJibizJkzXptQhlznyXEoWrQoEhMTefyIiIiIyClX2wvkGa3bW2FhYahcuTIKFCjg930TEQU7lqn+pXWZCngnBscguk6Yf7wJCQmIjo52elBNJhNu3ryJwoULs4eChtw5DoIg4Pbt27h48SIAoHTp0r5MIhEREREFAVfbC+QZLdtbJpMJ58+fR1paGipUqMBjTUTkZSxT/UvLMtWbMTgG0XXAaDTm/3hLlCih6jMmkwk5OTkoWLAgg+gacvc4FCpUCABw8eJFJCQkcGgXIiIiIlLkTnuBPKN1e6tkyZI4f/488vLyEBkZ6ff9ExEFK5ap/qd1meqtGByjrzpgHn8pOjpa45SQP5mPN8ffIiIiIiJH2F4IPeZhXDj/FRGRd7FMDU3eiMExiK4jvH0ktPB4E5EaV68CbD8TERHA+mMo4bEmIvIt5rOhxRvHm0F0IiIindq9G0hMBDp21DolRERERERERKGLQXQiIiKd+uEHIDcXWL9e65QQERERERERhS4G0cltFy9exNChQ1GhQgVERUUhMTERnTp1wtatW7VOmtdUqlQJM2bMcLpednY2RowYgfj4eMTExKB79+44e/as7xNIRERERKRTbC9YfPHFF2jTpg2KFCkCg8GA69ev+zxtREQUXFiuiq5evYoRI0agRo0aiI6ORoUKFTBy5EhkZGT4NG0RPt06BbVHHnkEubm5mD9/PqpUqYILFy5g7dq1uHr1qtZJ87uXXnoJv/76KxYvXowSJUrg5ZdfRteuXbFr1y63Z/0lIiIiIgpkbC9Y3L59G507d0bnzp0xduxYrZNDREQBiOWq6Pz58zh//jw++OAD1KpVC6dOncL//vc/nD9/HkuXLvXZftkTndxy/fp1bN68Ge+99x7atm2LihUr4oEHHsDYsWPRpUuX/PUyMjIwZMgQJCQkoEiRImjXrh327t1rta23334bCQkJiI2NxaBBg/Daa6+hfv36+e/3798fPXr0wDvvvINSpUqhaNGiSElJQV5eHl555RUUL14c5cqVw1dffWW13XPnzuHxxx9HsWLFUKJECTz88MM4efKk3XY/+OADlC5dGiVKlMCwYcPyZ+pt06YNTp06hZdeegkGg0FxEoKMjAx89dVXmDZtGjp06IAGDRrgm2++wf79+7FmzRoPv2kiIiIiosATDO2F559/Hj179vS4vQAAL774Il577TU0bdrUg2+ViIhCVTCUq88++6xX4nB16tTBDz/8gG7duqFq1apo164dJk+ejF9++QV5eXkeftPKGETXIUEAbt3S5k8Q1KWxcOHCKFy4MH766SdkZ2cr/B8CunTpgvT0dKSmpmLXrl1o2LAh2rdvn3+VbOHChZg8eTLee+897Nq1CxUqVMBnn31mt61169bh/Pnz2LRpEz788ENMnDgRXbt2RbFixbB9+3b873//w//+9z+cOXMGgNjTo23btihcuDA2bdqEzZs3o3DhwujcuTNycnLyt7t+/XocP34c69evx/z58zFv3jzMmzcPALBs2TKUK1cOkyZNQlpaGtLS0mT/z7179yI3NxdJSUn5y8qUKYM6depgy5Yt6r5QIiIiIiKV2F7wX3thw4YNHrcXiIhI31iuBlYcTk5GRgaKFCmCiAgfDroikF9kZGQIAISMjAy79+7cuSMcOnRIuHPnjiAIgnDzpiCIPyP//928qf5/Wrp0qVCsWDGhYMGCQvPmzYWxY8cKe/fuzX9/7dq1QpEiRYSsrCyrz1WtWlWYNWuWIAiC0KRJE2HYsGFW77do0UKoV69e/utnnnlGqFixomA0GvOX1ahRQ2jZsmX+67y8PCEmJkb49ttvBUEQhDlz5gg1atQQTCZT/jrZ2dlCoUKFhJUrV1ptNy8vL3+dxx57THj88cfzX1esWFGYPn264ndgNBqFL774QihQoIDdex07dhSGDBmi+Fnb407uycnJEX766SchJydH66SENB4H33j9dUv+7Izej4GjclAP29P79xcKeAz0gcdBe3LHgO0F/7cXjEaj8OSTT3rcXrC1fv16AYBw7do1h+s5aivovUwVBOYlesBjoD0eA32wPQ5y+SvLVd+Wq7///rtw7do1oV+/fl4vVwVBEC5fvixUqFBBGDdunOI63ihX2ROd3PbII4/g/PnzWL58OTp16oQNGzagYcOG+VeQdu3ahZs3b6JEiRL5V8wKFy6MEydO4Pjx4wCAI0eO4IEHHrDaru1rAKhduzbCwiyna6lSpVC3bt381+Hh4ShRogQuXryYv+9jx44hNjY2f7/FixdHVlZW/r7N25WOWV66dOn8bXhKEASHt3QSEREREQWzYGgv1KpVy2ftBSIiIlcEQ7nq7ThcZmYmunTpglq1amHChAlub0cNTiyqQ9HRwM2bjtcxmUzIzMxEkSJFrE5qb+zbFQULFkTHjh3RsWNHvPnmmxg0aBAmTJiA/v37w2QyoXTp0tiwYYPd54oWLZr/3DbQLMjcyxIZGWn12mAwyC4zmUwAxO+nUaNGWLhwod22SpYs6XC75m2oVapUKeTk5ODatWsoVqxY/vKLFy+iefPmLm2LiIiIiMgZNe0FX+7bFWwvEBGR3rFc9W25WqJECYfbdbdcvXHjBjp37ozChQvjxx9/tNu2tzGIrkMGAxAT43gdkwkwGsX1vBhD91itWrXw008/AQAaNmyI9PR0REREoFKlSrLr16hRAzt27EDfvn3zl/31118ep6Nhw4b47rvv8idScFeBAgVgNBodrlOvXj1ERkZi9erV6N27NwAgLS0NBw4cwNSpU93eNxGR2vHxiIgotKhpL+hVoLQX1Dbo1bQXiIhI31iu+rZcNXcEVkNtuZqZmYlOnTohKioKy5cvR8GCBd1Ktyt0FH6lQHLlyhW0a9cO33zzDfbt24cTJ05gyZIlmDp1Kh5++GEAQIcOHdCsWTP06NEDK1euxMmTJ7FlyxaMHz8+/wc6YsQIzJkzB/Pnz8fRo0fx9ttvY9++fR4Pg9KnTx/Ex8fj4Ycfxh9//IETJ05g48aNeOGFF3D27FnV26lUqRI2bdqEc+fO4fLly7LrxMXFYcCAAXj55Zexdu1a7N69G08//TTq1q2LDh06ePR/EBEREREFIrYXrKWnp2PPnj04duwYAGD//v3Ys2dP/kRvREREjrBctbhx4waSkpJw69YtzJkzB5mZmUhPT0d6erpPL2yzJzq5pXDhwmjSpAmmT5+O48ePIzc3F+XLl8fgwYPx+uuvAxBvyUhNTcW4ceMwYMAAXLp0CYmJiWjVqhVKlSoFQPyR/ffffxg9ejSysrLQu3dv9O/fHzt27PAofdHR0di0aRPGjBmDXr164caNGyhbtizat2/vUs/0SZMmYejQoahatSqys7Nlb3EBgA8//BCRkZHo3bs37ty5g/bt22PevHlW4zwREREREYUKthesff7550hJScl/3apVKwDA3Llz0b9/f4/+FyIiCn4sVy127dqF7du3AwDuueceq/dOnDih2AvfUwZBqZQnr8rMzERcXBwyMjLsTp6srCycOHEClStXVn37ga/GRNeDjh07IjExEQsWLNA6KU55chzcOe5kLzc3F6mpqUhOTvb5+FekjMfBN8aOBd59V3zurLTW+zFwVA7qYXt6//5CAY+BPvA4aE/uGLDeaM0f7QWt21uOjrney1SAeYke8Bhoj8dAH2yPA8tUe74uV7UuUwHvlKvsiU6aun37Nj7//HN06tQJ4eHh+Pbbb7FmzRqsXr1a66QREQWU774zYNiwzpg50wDJ8HZELpk2DUhJAd5/Hxg6VOvUEBGxvUBERORNLFfdF1xdmCngmG81admyJRo1aoRffvkFP/zwA8cSJyJy0dCh4cjMjMLzz3MYKXLf6NHAjRvAqFFap4SISMT2AhERkfewXHUfe6KTpgoVKoQ1a9ZonQwiooAxZgxw8CDwxRfApEnAhQvA3LnA7dviRDA3b3o2IQwRANy+bb/syhVg4ECgQwdg+HD/p4mIQhPbC0RERN7DctV97IlORKQTc+YAUVHA//4H5OZqnRrSo4wMYOpU4LffgOnTgVmzgJ9+Atat0zplFKxycoCRI4HZs4GvvgJ+/hkYMULrVBEREREREfkXg+hERDoxaJAYsJo1C1i5UuvUkB7YTiYqvbjywQfyy4m8KTUV+PhjYMgQsSe61OLFwPjxQGamNmkjIiIiIiLyFw7noiMmk0nrJJAf8XiTIxkZWqeA9EAaRN+4EVi6VH49ZifkK9evW57PmWN5npUFPPmk+LxcOfEOGiLyPdYfQ4dgeyWdiIi8imVqaPHG8WYQXQcKFCiAsLAwnD9/HiVLlkSBAgVgMDge09ZkMiEnJwdZWVkIC+MNBVpx5zgIgoCcnBxcunQJYWFhKFCggI9TSYHAtp1kNGqTDtKvNm2U3+P5Qr4izZsuX7Y8z8mxPOdFPyLfc6e9QJ7Rsr0lCAIuXboEg8GAyMhIv+6biCjYsUz1P63LVG/F4BhE14GwsDBUrlwZaWlpOH/+vKrPCIKAO3fuoFChQvyxa8iT4xAdHY0KFSrwIggBsA+i86I4ucJZEF0QgG3bgPLlxV7DRGopdYSU5lFbtojrsTpC5DvutBfIM1q3twwGA8qVK4fw8HC/75uIKJixTPU/rctUwDsxOAbRdaJAgQKoUKEC8vLyYFTRpTA3NxebNm1Cq1at2DtBQ+4eh/DwcERERPACCOWzDZqzZzG5wvb8OX8eKFAAiI8XXy9dCvTuDRQpIo5rHcHSnzwkPeeWLweWLQMeeUS79BCFAlfbC+QZrdtbkZGRDKATEfkIy1T/0rpM9VYMjs1oHTHfrqfmhAoPD0deXh4KFizIILqGeBzIW2yDoOyJTq44e9b6ddmyQMGCwLlzQPHiwIED4vLMTHEs68KF/Z9GCkxKPdFtl48bxyA6kT+40l4gz7CeT0QU3Fim+k+wlKkcR4KISEPmQJTtxW9eDCdAOYBp69gx+2VZWcDJk+LzO3csy3mBhlyhZjgXQLzzQenznBuPiIiIiIgCHYPoREQaSUsTx6iuXx8oVcr6PQbRyRW5ufLLzcFzBtHJ22zPo1u37Ne5cAGoUAG4917g9m3/pIuIiIiIiMgXGEQnUmH3bqBWLeDjj7VOCQWKZ54BmjUDbt5UXmflSnG4jb17gRs3rN9joJMuXADef1/dunl58svNgctvv7UsY69gcoXS+dK/v/Vrubxu1y5xqKF//wViYoCHHuIFQiIiIiIiCkwMohNB7EH38MPAE0/IB6NSUoB//gFGjvR/2ijw3LkDfP01sG0b8Mcf9u/PnQs0bw5s2KC8DQbRafFi9es664mekGBZxnOLXKEURF+xwvr1xYvihR8p23NtxQrgxAnvpY2IiEjvLl0SLyJPm6Z1SoiIyFMMohMB2LIFWL4c+O47YP9++/cvXvR/mihwSQNHycnAb79Zvz9gALB1KzB/vvI22FuTbO9OcESpJ/rDDwMtWogXAc0YRCe1cnOB555Tv75tYF0uH+OdEEREFEwEARg7Fhg4UP6urLlzxfJx9Gj/p42IiLwrQusEEOmBtBcng5fkKdsg0YQJ4pjnc+cCgwap2wbPw9C0cSOwZAlQowbwxhvqP/frr8rvbdli/ZpBdFJr/XrXzpecHOvXcp9lEJ2IiILJyZPAu++Kz7t3FzswSF2/7u8UERGRrzCITgTrhr5cA5+NfnKF7fmSnS0OFXT8OPDnn+q2sWuX99NF+teli/wEjd7EIDqp5eq5aDusEIPoREQU7LKzrZ/PnSt2hhk4EDAYxD8iokDw/ffiEFRDhwIRjBbL4nAuRLBu6DPARJ6yPYcOHBAD6IA4iaga5mERLlwA5swB0tK8lz7SL18H0AFxklGlMdSJPGE7rBCHcyFAHE5q2TLWr4goOEnLuv37xWEbBw8Gdu8Wl7HcI6JAkJYGPP44MHw4sG6d1qnRLwbRicCe6ORd3jhfihUTH595RhwCpk8fz7dJBAAvvwy88grzNXLMnZ5z7IlOcpo3Bx55xPGwU0REgUpa1l29anmekeH/tBARuSsz0/Kcw1ApYxCdCNY9CNjAJ085OocKFHBtGytXio/r13uWJiKpjz4SJ1MmcsTVnsPsiU5mWVnA0aPic3ND7MABzZJDROQz0rJSWg6yvCOiQCLNs3j3oLKADqJPmTIFjRs3RmxsLBISEtCjRw8cOXLEap1ly5ahU6dOiI+Ph8FgwJ49e+y2k52djREjRiA+Ph4xMTHo3r07zp49a7XOtWvX0LdvX8TFxSEuLg59+/bFdV6eCRrsiU7e5KjQiYpStw2ec+Rra9eylxQpMxjsg+LOsCc6mXXpAlSvbt37PCygWx1ERPIYRCeiYMAgujoBXZ3duHEjhg0bhm3btmH16tXIy8tDUlISbkkGlb116xZatGiBd81TZst48cUX8eOPP2Lx4sXYvHkzbt68ia5du8Io6UL11FNPYc+ePVixYgVWrFiBPXv2oG/fvj79/8h/GEQnb3J0vlSurG4bLLjI1z7+GChVCjh0SOuUkF65GkS3XZ9B9NBlHktz8WLLMqUgutwdC0REgUJa9kmfsy5PRIFELohuNDIvsxXQ862uMM+8d9fcuXORkJCAXbt2oVWrVgCQH+g+efKk7DYyMjIwZ84cLFiwAB06dAAAfPPNNyhfvjzWrFmDTp064Z9//sGKFSuwbds2NGnSBAAwe/ZsNGvWDEeOHEGNGjV89B+Sv3A4F/ImR+eQXCHUpAmwfbvz9Yi8LTsbSE0FatXSOiWkR572ROdwLiQVHm6/bOpUICVFDLZ36+b/NBEReSI3F3jgActruZ7oLPeIKBDYBtEvXwbq1RPrb/v2AUWLapY0XQnoILqtjLv3pRcvXlz1Z3bt2oXc3FwkJSXlLytTpgzq1KmDLVu2oFOnTti6dSvi4uLyA+gA0LRpU8TFxWHLli2yQfTs7GxkZ2fnv868O0p/bm4ucm1bmW4wb8Mb2yIgJ8cA88/hv//yMGhQODp1MuHyZQO2bDFAHN1HnGVN+p3zOGhPb8fg118NePTRcJjPF1u5uQLi4oCMDMv7YWEm2N4YZDQKyM3NAxAp+aw+/kc5ejsOgSvS+SpeJghG5Ob6/qqNp+cGy1XfE/918Rw0GARkZRnhSlXxnXeAQYNyUa6ceXuWstUsJyfXLthu2T+PgR545ziI55HJJC3f7POaMWPE9V55RUDnzi5etQli/C3og56Pg97LVPO2pI/B6MQJQFp3y8625Hm5uXnIzRVgNIYBCL+7zL/fRSgcA73jMdAHHgfncnIAc36Wm5uHXbuA8+fFevy+fXlo1syzK4J6PwZq0xU0QXRBEDBq1Cg8+OCDqFOnjurPpaeno0CBAihWrJjV8lKlSiE9PT1/nYSEBLvPJiQk5K9ja8qUKUhJSbFbvmrVKkRHR6tOnzOrV6/22rZC2Z495QE0BABMn34VR44k4MgRmS5TAFJTU+2W8ThoTy/HoFevhx2+n5l5C0ZjAQAFJMuuAoi3Wi8rKwepqSsAWLYnd+7pjV6OQ+ByfP74wpEjh5Ca+p/P93P79m2PPs9y1ffOnCkMoD0AIC4uC3v2/AugnkvbmDlzL1q1OgcA2Lu3IoD6Vu9v2rQZZ89mOtxGKB8DPfHsOIh52dmz5wGIV1UOH5bLa8T1Ll7MRmrqyvyly5dXwfbtpfHSS7sQH5/lQToCG38L+qDH4xAoZSqgz+9PjTt3wjF1amNUqpSJZ56xH/tOEIA33mgOoGT+snPn0gGUAQDs2LETeXkX8d9/NQFUB6BdXT5Qj0Ew4THQBx4HZSdPxgJoBwDYs2c/zpy5A6A5AGDz5q24du2qV/aj12OgtlwNmiD68OHDsW/fPmzevNkr2xMEAQaDpaeo9LnSOlJjx47FqFGj8l9nZmaifPnySEpKQpEiRTxOX25uLlavXo2OHTsiMtL/PReDzeXLluMYHR3vYE0gOTk5/zmPg/b0dAy2bpXPD6SiomJgmz+XKGF/90xERAGrcw2A3Ws90dNxINfUqVMLycn3+nw/5l5u7mK56nv791ueFy1aEPfeq65TwqxZeRg6VKxS1q5dH6mpDZCVZcB999n3WGnR4kE0aCC/HR4DffDmcShVqkz+c0d5TcGCUVZlXI8e4n6PHu2Afv1Cb3wz/hbU+eSTMKxfb8B77xlRtar3t6/n46D3MhXQ9/enxpIlBuzeHYHdu0vh228r2c3rcOECcOCA9f9VvHhi/vNatRpjyZIw/PCD5YP+rssH+jEIBjwG+sDj4Ny+fZbnderURenSltcPPNAMrVt73hNdz8dAbbkaFEH0ESNGYPny5di0aRPKme8hVikxMRE5OTm4du2aVW/0ixcvonnz5vnrXLhwwe6zly5dQqlSpWS3GxUVhaioKLvlkZGRXj1hvL09wt1b7pTJfd88DtrT+hgsWwY88ojz9fLyDHbjBIfJzLZ25YoBGzda/z+BcI5pfRyCWUICUK0a8Oef3t1uZGQ4IiPl77zx7n48Oy9Yrnrf3LnAsWPApEnAnj3A0KGW90wmA1atUndeDB4cgUmTgHPngIEDLVXLXr3s142IiISzrzeUjoGeeeM4CIKlfHOU1xgMBtl93bzpn/xJr/hbcOyll8THKlXCMH267/ajx+MQKGWqr7bpD9L6eliYfdkl15fOZLLkeX/8EYGFC63f1+p7CNRjEEx8eQyMRmDiRKBSJWDgQJ/sImjwt6AsQhIdNhgirPK4sLAIp/V3tfR6DNSmyXG0UOcEQcDw4cOxbNkyrFu3DpUrV3Z5G40aNUJkZKTVLQVpaWk4cOBAfhC9WbNmyMjIwI4dO/LX2b59OzIyMvLXocAmncSREzqSO9QE0AHg7Fn159hDD7mfHgo+9epZV268ReYaDoWAnBxgwABxHPN584D77wd27bK8bzIBu3er25bBAMh1XJS7K5ITrIUWaXknN7GoM8yfSI0bN7ROAQUjaQBJ7UTZK1ZYnkuGnCfyqT//BN5+Gxg0iOcduc92YlHpRMlyeWCoCuie6MOGDcOiRYvw888/IzY2Nn988ri4OBQqVAgAcPXqVZw+fRrnz58HABw5cgSA2Ls8MTERcXFxGDhwIF5++WWUKFECxYsXx+jRo1G3bl106NABAFCzZk107twZgwcPxqxZswAAQ4YMQdeuXWUnFaXAI23kMYMgX7MNLCmMCmVVcBEZDL4JorsT2KLAJ82HBg2yf18QXCsP79yxXyZOUGS/XQod0vqVo4B4Xh7w9ddiL7pWrdR9hsgVggAsXSqeY40ba50a0rP//gNWrrQu12zLw82bgfXrHW9HrtPM9u3AwYPAk08Cd8MVRKpduAD88IPY0erYMeDUKbGuJb2pJC/P+jUAXLok5n8dOwL33OPfNFNguHkT+Oory2uTyTrfY4zMIqCD6J999hkAoE2bNlbL586di/79+wMAli9fjmeffTb/vSeeeAIAMGHCBEycOBEAMH36dERERKB37964c+cO2rdvj3nz5iFcEllYuHAhRo4ciaSkJABA9+7dMXPmTB/9Z+RvDKKTlpSC6ERSBgPQoAGwdq3j9f7v/4yYNy8cf/+tbrsMUoUmuaC3lMkEXLzo2T4YRCfr4RCU17t4EXjmGfG5tFcx8yeSc/YsFMu4nBwxABodDTRvLgY6W7YU76zp3Vtch/kQOdKjh/UcIYB1XpaRIZ5TzsgF0Zs2FR+zs4HnnnM7iRRijh0DduwAPv4Y2LYNqFABOH3a8r60LXn4MNCokfXnR44EFi8Wn+/YwQuJZG/KFPH8MrPtic7RGiwCOoguqKgB9e/fPz+grqRgwYL4+OOP8bH0rLFRvHhxfPPNN64mkQKEtGLEDIL8jY05UiMsDBgzRsyvtmwRezPJ+d//TIiICMfzz6vbLnuihyZnE9CfO+f5PhhEJ2n9Su0F41u3LM8ZRCc5LVsCJ09aXkvPrS++AEaMEJ/Xry/O99C3L9C+vR8TSAHNNoAOWOdl336rbjuOOmZ5o4yl0CAIYl4mLRulAXTzOmb33w9kZVn3Rpd2wHngAeDMGcDFqQQpyC1dav3atid6VpZ4MbpePdbNQvzfJxKxJzp5wtNzhkElUiMsDIiPBz78EGjb1vm6RI4464nuDQyik7R+pfbYq+29TqFLGkAHxCD6hQvi+SYNTu7ZIz4uWGC9PvMhcpU0X1JbfjpqH/AuVFLLaLQOoKsxYYL1a9s8Ly3NszRR8MnNtX5tNFr3RB8yBGjYUGyHhjpWTSmkmRv4nFiUPGFb6LiKjTlSw3qGdPl1ChQw2r2/ZAlgM+qZFeZ5oenuiHhONWzo/j7MASwp5nfBT3rxxNMgOu+UITXWrAFKlwYef1xdxwbmQ+Qqd8YGdlS/YhCd1HKns9bVq47f97TtSsHH9pyw7Yl+5Yr4+O67/kuTXjGITiFr0SKgcGHxahonTSBPqK2I3J1SwQ6DmKSGNDCuFER/4YW/7d6vWNHx5FcMJoSmzEx169lOTuUpnm/B7aWXgLg4y+vVqy3P1R57tZOREpmdOGGZOFRNnYr1LnKVO21F9kQnb+jY0fXP2OZxtuUvg+hkS9rrHLAfE91M7i7TUKNZ1fTMmTP4448/sHLlSvz999/Izs7WKikUol58USxAXn6Zw7mQZ9RURDZsAIYPl3+PQSVSQ9rgatMGiImxfn/Rojy0aHEegLqAuxmDCaHp5k116xUsqG49tflY8+ZAhw7M94LVjBniuJlyhgwROy9cuOB4G9JGG4Po5Cr2RCdfYE900soff7j+GdtzlEF0csZZT3Qzxsr8HEQ/deoUxo4di0qVKqFSpUpo3bo1HnroIdx///2Ii4tDx44dsWTJEpjYoic/kN7mJJ1sw1HG4O0eeRQc1FREwsKAAgUsr6WTQppMjofbMKtdG/jhB5eTR0FC2uBq3x7IyBAneDGLjLQ8dyWI/uKL4vmntmcyBQe142s6CqJHRABPP+36vteuBSpXBg4fdv2zFNhu3RLHp27XDujcWX4daS8nBtHJVQyiky94uyc6kS85C6d17Ah8841/0kL6JwiW4VrMlHqiM1TrxyD6Cy+8gLp16+Lo0aOYNGkSDh48iIyMDOTk5CA9PR2pqal48MEH8cYbb+C+++7Dzp07/ZU0ClHSCnR0tOW5owoPK90k58AB5+uEhVkHOQsVsjwXBHE8z+rVHW/j0CFg2DD30kiBz7bXUni4OFSL+eLePfdYMii5IPpDD4mPpUpZb+fOHWDjRiA11csJJl2TDrPhSFQU0LKl/fI2bYDLl4Gvv3Zv/6dOib2WKfS88oo4xNTKlfLvz55tec4x0clVahr4H3zg+3RQcJGOA+yNIDoDUeRLRqM4ZO1DD4kdFuRiGIMH+z9dpE8XL9ovM5mArVvtl/PioB+D6AUKFMDx48exdOlS9OvXD/feey9iY2MRERGBhIQEtGvXDhMmTMDhw4cxdepUnDp1yl9JoxAlLUzUDufCIDrJUROMCg+37okuvatBEMT3Y2Odb8fZbfAUvOR6ZBYrBpw+Lf7Vri2/rvn5Tz8Bx48DDz8sv/3bt72WVNI5V8qyypWBVavsl0dGimNfO7slvXhx5ffU9oan0DJ9uuX5d98BI0bwXCH11DTwx43zfToouMyaJZ5bc+cCKSnqPuMoUM4gOqnhbuzBaBSHrF2xQpwg/to1+3WUhl6j0CNXbppM8uOfM4gORPhrR++//77qdZOTk32YEgplly8DH38M9OxpvVx6q0pamvLnc3PFQJO05zqRO8O5SIdIYEWaAMDZDVhKwxokJIiP0vNQGtg0Py9QAKhSRXk7crfsUXBypVH2zjvyQ7rY9hBW2qZ06DRbai4cUuDwRaD70CHx77772GuO1Pn8c61TQMFq/35gwAD167NjFnnK3fNEWh7fueOdtFDwkjvPlOITjFtoNLFou3btcP36dbvlmZmZaNeunf8TRCFj4kRg0iQxiC7NLFwJHv3yi/h46RLw2WdhuHqVA6WHojt3xIba+vXqruSHhQElSlhex8VZnrMiTYByD3EzVyahcjQmutJ2OMlQ6FBbAe7QwRJAt50TJMKmG4Y7+ZijXuoUeJYv9922HXVwICLyhy+/dG199kQnT7nbRpQbnoNIiVx+ZDKxbajEbz3RpTZs2IAcmXsDsrKy8Ic70w8TqWQ+vU6etF7uShDdPPneqFHAN9+E4/7767s1sRoFtsWLgeeeE58/9ZTz9WNigKpVgUWLxN6X0iA6K9K0davzIJG3gujsiU7OGmWpqcCePdZ526ZN4tj5r74qvvbGWNWOJi2lwHPjhu+2zXKSiLSWne3a+hwTnTzlbhB9+3bvpoOCm9x5dvmy/HAu5Ocg+r59+/KfHzp0COnp6fmvjUYjVqxYgbJly/ozSRRilIJHrly7MVd6zDNa//VXIrZty0PTptYTR1Jwk44tZ3tRRmrAAHGs6nvvFV8/+aT9OuXKeTVpFGDS0oDmzZ2vp5R/yZEGJ217ECtth70NQoejxnuvXuJEVOaJaM0eeED882YQnUGE4HHnjvwEVN7CMTjpyBGxE0KZMu593vZCtCC4dnGaiEF08hfzpI5VqmidEgoFckH0DRuAxET59S9fBuLjfZokXfNrEL1+/fowGAwwGAyyw7YUKlQIH3/8sT+TRCHm3Dn55a5cZZOrELVqFYExY6xnbqfgJi1stmxRXm/OHOX3liwBli4F3njD/r06dYADB5T3zYZf8FB7y6Urx7x9e2DIEKBkSaBSJXXbYRA9dDhqvI8cqW4btsO5eDsdFFj69we+/9532+e5EtpOnBA7I8TGOp5nQUmDBvZlLetS5CpXg+gczoXc9dlnwPDh7l80JHKFXBC9fHnluW6uXBE7TxQtGprzG/l1TPQTJ07g+PHjEAQBO3bswIkTJ/L/zp07h8zMTAxwZbYOIhcsXSqOY+4po1GcXNTWe+95vm0KHI4qv7Y9f5U8+qg4LEzt2uLrIkUs782Yofy5N99Ut30KDGqHUXGlsR8TA8yaBbz9tv3nlHqiL1mifvsU2BzlX2rveGBPdJLyZQAd4LkS6o4dEx9v3HBvAtv0dOCVV6yX8ZwiV6mZA0mKQXRyl7mD1vnzvttHRASQkeG77VPgUJpYVKmD1a+/AhUqANWrh+aQL37tiV6xYkUAgImlBmng//7PO9tZtAh46SXvbIsCl6NsrEAB13urAMC4ceLdEn37ij2J27QRb6WydfCg69sm/VIbRPfWcFFKwfjy5b2zfdI/R/mX2uA4e6KTP3E4l9AmbeC7cy7IzTnC/IfUKlwYuHnTcd3eYLAPRHE4F3KXK0M4uisvT+xJPGUK8Nprvt8f6ZdcEF0QlAPko0eLj+npYuyicmXfpU2P/NYTffny5ci9eylj+fLlDv+IfMHdBliTJtavt21T3pY7vWMoMDma6KVAAfe22b69OObn+PHia6WgKSeADC7OhlF58kmxkvvMM97Znz8q5qRv3uiJbhtEd2fyKwYRSC2eK6HN0yC6HJ5TpFbPnuKjUk/0SpXEIfRsOZongucfOaL27lNzMNMVI0ZYv377bde3QYHr+HGgbFlg8GDLMrk6fEYGsHu38+2tXy/+FS0K9OnjtWTqmt96ovfo0QPp6elISEhAjx49FNczGAwwsrsJednBg47HrXbElSEUDh4UJ16j4OesJ7o3KPX0ZBYZXJxdFFm0yLv7U8rTODZs6HAU8HYWRG/bVpyMu1cv5XXeekt+rgdbDCKQWjxXQps0z0pI8M42zefU4MFiB5nvvgNq1fLOtim4FC4sPir1RA8Lc72DAvM0ckTt+eTOXaq2n8nOFoelnTMH+PhjoFMn17dJgWPBAnGYoC+/BGbPFpfJtQv27lW3vcuXxTvnMzLENuvChV5Lqm75rT+ayWRCwt1aj8lkUvxjAJ18YepU9z/rSmCJE/OFDm+Mie6MdIx0KfZEDy7+Pp5KFXN3ehJTYPKkJ/qaNeLEfl27Kq9jvpvGk3QQSfFcCW2+KJ9MJvFW9S+/FCdy9/W4/hS4YmLEx9On5d9PS3P9HGWeRo6ojT94I4ielycO53L0KDBxouvbo8By5479Mrn8S21cy2QKvdEYeFM3hYRr19z/rCtB9FCcWCFUOar8emOsYAD49FP55bzWGFzkguivv+67/SkFSdmgCx2eBNHDwoDYWN+ng0iK5R65ato0scedEkGwzoOYH4W2TZuA3r3FHpWFClm/Zw6iK03yeOeOda/NMmWc7+/zz4EuXYArV8TXhw6JcyJt2+Zy0imEeSOILsWJRoOfXHBcaUx0NYxG9+aCC2SaBdHXrl2Lrl27omrVqrjnnnvQtWtXrFmzRqvkUJDzZEI+9kQnOf7otVu8uPwtUeyJHlzkjqd5WCjzLcTepJSnMYAQGm7fdtxT3J9j5vOcIylpflezpvV7v/3m37SQfuzYAYwc6frnatYUx31VYjIxDyKLZ54BliwBnn7avp5ke2dozZpA3brWyy5csDxXGjvdVmoq8OOP4vPnngO++QZ46inX0k3ByZ/DuUhxaMfgJ9fu9CSuwSC6n8ycOROdO3dGbGwsXnjhBYwcORJFihRBcnIyZs6cqUWSKMh50jOYPdFJjqOGV/Pm4mP58p7vp0oV+2XskRdc5CozrVoBq1YBu3Z5f3/siR7aFiwAZs1Sfr9YMf+lheccSRUtanluW287ccKvSSGdWLoUaNIEOHbM9c926uQ4EGUyAevWWV4zeBTaTp4UH8+ds3/Pdq6jOnWABg2sl3XrJj62aCE/XIKSmzfFx02bxEfmdQSoz4/ciXF4a+4uCky27c6bN4EZM9zfnskEbN/uUZICjt8mFpWaMmUKpk+fjuHDh+cvGzlyJFq0aIHJkydbLSfyBn/1RGcQPXQ4Cv588IFYue7Y0fP9yJ1/7IkeXMzHs3BhS2MqKso754+c8HD55QxohgZzoMDW4MHAQw8BFSu6t90OHYB58+x7EDvCcy54xMeLk0t5QlpXsw0M+PMOCdKH8+eBxx5z//POJns8eNAS+AQYRCcLaa/MxET78ygy0r7NN2ECUL26OF9ItWrq9xVqPThJHX+OiU6hxTaOMGOGOLyUu4xG4MYNy+uffxbL1mCut2nyr2VmZqJz5852y5OSkpCZmalBiijYeWuMamcYRA8dcsGfoUPF3sMlSwIvvgjUru35fuQqUeae6JcuiZP8cRihwHX0qGX8y8aNxYrM4sVAdLTv9vn00/LLBUHsPbVhAxt1oeiLL4CePd3//JQpwBtvuDY5H4PowcMbAUhHQXROfBx6vNEkdNSIb9nS+jWD6GRmWzbZdj6IjLSvexcrBjz/PFChgn2QqnRp5X2pHfqFQou3hnOpXNl+mVJnGkAcm5/xjOBme0f75s2ebc9ksj6nevQQ4yHBTJMgevfu3fGjeQAwiZ9//hndpF0CiLyEw7mQt8k16N97z3e9h6XMlfM2bcT9TZni+32S92Vmir2Wpk0TXwsC8MILwOOP+3a/ckMEAWIl6OWXgbZtxQtCRK5ITAQmTRJvc7dlnpTNFoPoge3cOSA9XXx+6ZLn25MGA2wb+YIg5pk7drCuFSq8kT+40hOOQfTQZDIB+/fbLzMzGOzPo4gIx/mQ7bnr6FxmEJ3kqM2PCha0XyYdpiopCbj3XuefkZowQd2+KTBJL/JlZgI7d3q2PaPRPi5y8KD9Ov/+GzwdIvw2nMv//d//5T+vWbMmJk+ejA0bNqBZs2YAgG3btuHPP//Eyy+/7K8kUZAzGsVbQcuW9d9wLgsWiDO7OyucKPDJVYh9cceDo57ohw6Jj+vXA2++6f19k2/ZDn3gyu2/nlDqgXLjBvDZZ+Lz+fOBDz8UJ7cl8tQPPwAyNyByfocA9t9/QNWqYn6yYIF3tukoiA6IE/mdPi0OO5Sa6p19kr7k5orByZgY7zS2GUQnZ1591dKZwcz23JMbzqVECfX7cHQuM4hOrujQARgwQByDOivLPkD+6KNiJyuz3FzruW769xfvfHVk8WJ20Apm0guAdesCV696tj25Sbpt88wRI8Q25vTpYbJ3RwQavwXRp0+fbvW6WLFiOHToEA6Zo0AAihYtiq+++grjx4/3V7IoiDVvLvZY6t5dvLXOXa5UqtesEW+bOn7ct8MxkPbkguiObo9zF8dED162twLLBRl9ZcIEICXFetmOHdavz51jEJ28o1MncXJA24mH2BM9cB09Kj4ajcBTT3lnm86C6KdPi4++mHCZtGcyAfXrA2fPij3W/N0TnULTzJn2y5wN5xIRIQbf581Ttw9H5/K8eYBNmIRCjMkktvekbT6lCy+rV4uPTz4pPtrW3cPCxO2MHw8sXy7eWZqZCbz0khh8f+kl+zsvbCnNnUPBYfFiy3NzvcoTch1ibMtecyetqVPD8p8HMr8F0U9wqmnyo927LYXKpk3AyJGufb5xY/EW5UceAfbude2z6enAmTNAjRqufY4Ci5ZBdPbeDA62F0P82Qtu4kQxoLlihfI6DHAGJ61upZQ7v3mOBS5fDKniLIhuxh7DwenmTcsddnv3AqVKeb5NV+amCZbbzMk1csfdWa/KyEhxEu169dS1E00m4NlnxTto3nwTePddIC1NrAcGQ69Mct+//4od/ypUEGMX5rua1daPbM9N8+u33hL/zKSBc1+0Vyl0yZ2rShewg6UjoObX5//8809kcwYz8jLJDQ64fl0cpxUAhg0Tb1txpk4d8crc9OnuNdZYEQ9+csfYX0H0I0d4jgUD257o/g4MDRrk+H0GOMmbGEQPfGvXisNOLVjgmwmtpUF09iAOPdJ6jdwYq+6IjhbvglHDaAS6dhUD7+yJSVJyPdHllisxmYCvvhI7Wj3/vNjGXLlSfI9zPIS2bduAK1fEDoCrVolzJRkMwJdfqvu8bd1KTVuC5St5k1znvt9/BypWtMTgzIKlE4TmP6GHHnoI586d0zoZFGSUKt4Gg3IvXumP2pMx1B3tn4KHbfBHbuIhb1AqbO6/3/v7Iv+yDUL5u1L7yCP2YylK8Y4H8iYG0QPfiy8Cx44B/fr5PojuqKF14YKYf/kiDaQdad05L897+UOBAurWO30a+O03sSPOhg3e2TcFB9v62a1b4qPauZDkzmXzecl8LLRJ69pLl1qGSpOTnGy/zPZOhoQE5/tU095gLIPUksvffv9dLFMnTABatrQsZxDdSwT+QskHHAXRlSrl0s+obcgpke7j1CngsccAydy6FKCuXwf69gUGDwZmzLB+z1dZmdL59/ffvtkf+Y+Ww7mYOZq7gQHO4HL7tjge5qefarN/ufP766/9nw5y35kzludaBtEBYNkyloPBxrYn+muveWe7aoPoN29anrP8IynboGODBuLj0KHi48MPW78/Zoz1a0dBdPZED23Sc8NZubp8uf2y4sWBgwctr9VMCFqqFBAV5XgddqQhtZwN0bJ5s+U5g+hEOqYU0AwLUy4UevSwXs/M0+FcZs0Sryy/8ILr2yF9WbkS+OYb+VvsevXyzT6l59/cuUDVqvLrffedOPb/hQu+SQd5n9bDuTjbJ4MIwWXZMjEPuXFDm/0rnWsc0S9wSOtGWgfRATbyg420zDl50jKBnqfU3l0qzRvZx4vMDAYgJsbyunZtYOBA8fmzzwJ79lhP1AcAkydbX+STO5/M52VODs+3UCYtx7KyHK+rNHxQrVrAf/8B588DhQo532dcnHhX2datyuuwDUBquVIfZBDdS2bNmoVS3pg5hkjCnZ7otWpZng8ZYv0Zsz17gA8+cL5/6T4yM52vT4HBUW+RYsV8s0/p+Ve+PPDee/LrPfEE8PHHwMyZvkkHeZ/tlXstxihkED007NwJvPyytmmQnmvvv295vmmTOP8Ig+n6J23AexJE37zZ0nNT2kvY1SA6A0/BRXo8zcNleIPanuhnz8qnhah5c8vzxo0t+ZPBIE4uWrCg9frh4Zbe6oDjnujnz1uC8raOHhUnIb140f20k75Jz43bt93fTuXKQOnS6tcvVw5o2lT5fV6kJrPHHnP8vit30zCI7iVPPfUUYqSXd4m8wFEQvWNH+ffatgUWLQJ+/VWcWFT6GbN69cRARNGijve/cCHwxx8uJZkCgBZBRen5Fx4OPPSQ4/UvX/Ztesh79NATXZrX2WIQPXh06qR9I1x6fnfrZnmelASMGgXMn+//NJFr3O2JbtszrkUL8S69L78Epk2zLJcGO9VM2Mc8KrhI6+7evKimNoguHRKBQXSSKlLE8tydc9NREB0Q7xIzkwbf+/QBxo713tBGpD/SYLUnQXR3Kd2pw/KVAOC+++w76Nnekc8guh+kpaXhm2++QWpqKnJsvvFbt25hku0UrkRucBREVzrFoqOBJ58EunSxXl63rvgorexIC7whQ/ahXTvrkmbqVKBVK/vb5lkpD2xaHD/bIDpnVA8e165Zv9aiYjF5MvDAA/LvsQIdPGzPNX8wn1fmHnxNmljek+s7sW+f79NEnpEGtjMy1H+ucWP7ZaVKib0vS5SwLJM25tWUdcyjgou0juXNcaLVDucixXOLpKQTiMbHq/+cefL2du3s31M6L6W/g507xcdfflG/Twos3uqJ7i6lC+LsiU6AODqDdLLaYsXs75wJxeFcVM4p7R07d+5EUlISTCYTcnNzUa5cOfz444+oXbs2AODmzZtISUnBm2++6c9kURByFEQvWRI4cQLo18/SW7xpU+sGvtSYMWLmIJ00RppZJCefAFAL69bZt/ikkxSZ0xUsmUcoYhCdvMn2yr4Wx7ZMGeCtt8SeyrZYgSZPzJ0rTmI6bJj4esIEMTBWuTKQmGi/vrOxQEl70jxqxQr3PmdL2kFBOiSCmroSA53BxVc90ZXqbm3bAuvXu/YZCj7O8hrz+0uWAGvXAq+8on7b334r9tp88UX795Qmdme+Flq07omuhG2A0FGqlNi55b//7N+zvStQ7rxIS1O/r2CJg/m1yf7666+jV69euHbtGi5cuICOHTuidevW2L17tz+TQSHA0cSiAFCpErBhA/Dcc8Azz4iVaKVbh0uWBD76yLoXQc2a4mOhQuKO2rSR36FtRYiV8sCm9XAuYWHywQieV4EpLs76tVYVC6UAFxty5IlatcQLRebyMiYGmDFDnGRbrrxlEF3/pMfNlemMbCfIlurQQRyyYPRosW4m9xklzKOCi/R43rnjfH25OYqGDgUefRRYvtyyrHBh+/WaNbM+32yxXhU61B7rRx8FPvtMnJ9Irfr1xXLwnnvs35P27pRivhZapMf7wAHt0mGL52HoaNdOuc6lJoi+bZv6fTGI7oZdu3ZhzJgxCAsLQ2xsLD755BO8+uqraN++PXaa71ci8gJHPdHNwsLEXnLz5tlPCOPMzJliBXzhQjEniY4Gvv/eeTpYKQ9sjo6fXCPNG9T0ROd5FZhsK6gMopO/FSoENGokloX+JHeuM4iuf9K8wjxcXbNmzj8nPd79+1u/FxcHfPONONmstLHGIHrokdZlZs1yvv6wYcDnn1svq1JF7DEsnXehUSPP0kLkK3//bb+M+Vpo0WuPb072HpzkyjZHd7rbBtHLlfNs/wyiuynLppX06quv4vXXX0dSUhK2bNni7+RQkFITRPdE8+bAli1A166WHT32GFCxovV67IkeXJSOX/XqwPPP+37/4eHy5zDPq8Bkmz9oNVSPUr7Ihlzw69AB+Osv8a4sf7M933/4wf9pINdIj9nKleKj7aShzj6ndj019TW9Bh/IPa7WZcLC7M8TufNGzSS1tlj+hQ4tgzoNGoh3Rkvx3Astcsf71Vf9nw5bZcsCjz+udSrI2+TK2YgI5z3Rv/5avHvro4882z+D6G6oU6eObKB89OjReP311/Hkk0/6MzkUgnz9w7XdviBYZ1YMdgY2uYpOz57AkSNiIN0XbHuiM4gePNgTnbSm5RwLnN8h8MgdMzV38qnN22zvFjRr1Up+feZRwcUbQfSGDe3XcyeIznoVmUX4eAY52wlGma+FFrmLwUrj5fub3F32FNiUeqJLy9IdO6zfA4C+fcU5BeXm0HIFg+hu6NevH/7880/Z91555RVMmjQJFSpU8GeSKEg5GxPdV2y3z57owcX2+IWF+b4Hp20QXQ7Pq8DEIDppzZ3gkrcwiB545M4XV4fDU0t6figFsZhHBRdXj6dcEL1tW/v15M5bg8Fxmct6FZmHARoxwrf7YRA9tMkdbzV3eBG5Q+58sx3ORfrc2+0EBtHdMGjQICxYsEDx/VdffRUnTpzwY4ooWPl6OBcltttnED24SI/fPfcAly8DHTv6dp9qgujS8yxYCqdQoJfhXJT2y6ESgp+W+YWWAXxyj7s90d3J21wt+yjwuVpHNhiA2FjL65gY+XNN7vzp0cO7aaHgMHCg5fmiRUBaGvDyy77dp20Q/dgxYPNm62WXLwOHD/s2HaQNubp2Xp7/00GhQa5ss72o7E4QvXVr4P77na8XLHEK9gOioKSXIPqFC9YTtrFSHtikDfYCBYBixXy/TzWBhK1bLc95jgUOvfREL1tWfjkDVMGPw7mQK+TKoKpVnX9Obd5WqZL4GBUlzj0DAAkJyuszjwourtRfihUTz6v27S3LoqLk17U9b5OSgNGj2ROd7BUtankeGQkkJvp+n7ZBdEA+cP/KK75PC/mfXDnWurX/00GhQe58sw2iq4k92CpWTJzfoWlTy7LkZPl9Sf36q3i3z/Xr6vajF7pqwvzzzz+oUqWK1smgIKBVEN02KPDll9avWSkPbNLj99hj/tmnNFDvj6A9+Y9egujmwJUtBqiCn96C6Cwj9S0+3n5Z8+ZAkSKOP6c2b+vZE1i/Hjh4UGxUrV4N7NqlfF4wjwourvz+9+wRH4sXtwQhy5WTX1caBPjf/4Dly52fkzy3QpP0Qoy/7paSy1flbsw/eND3aSH/k+uJLndhxVfMvYcff9x6LGwKTmpiZO70RDcYxLvB7rnHsszZHV8A0K0bMHMm8Mkn6vajFz6eKsM1OTk5OHXqlNbJoCCg1ZjotpXyK1d8uz/yL2mjyl8zp8fHi1d2AaBkSf/sk/xDL8O5KGEQIfhpec5lZdkv4zmnb3JByshIcZiBdeuAp5+2fu/LL8VxhefPB37/3XljLCwMaNPG8rpDB8frc8ip4KL291+6NGCeQissDNi+HdiyRXnCM+l517GjJVDKnuhkSxrQlg4V5EulSokXD69cAZYsAb77DnjgAfv1gmUYBLImV45FRIj5VHa27/f/ww/AmjXAww8DJUqIHbauXfP9fkkbanqix8VZnkvvznF1+66M7R9oI3r7NYg+atQoh+9funTJpe1NmTIFy5Ytw+HDh1GoUCE0b94c7733HmrUqJG/jiAISElJwRdffIFr166hSZMm+OSTT1C7du38ddq0aYONGzdabfvxxx/H4sWL819fu3YNI0eOxPLlywEA3bt3x8cff4yirp5Z5Bd66YluO6YZK+WBzXz8nnjCvzOn87a+4KSXnuhKGNAMflqOS56ba7+M55y+ydVhihQRg5p9+gAvvQSYq/K//Wa5lbdcOTFQ1KqV9/YL8HwJNmrryLZlZYMG4p8SaT6n9sIh6+uhqUoVYOFCMfjjz7s/zRcPb98Wg+hXr4p3TEjprY5I3iFXjkVEABs3ij3Dv/nGtz3EK1QABgywvNZbhx7yHkEA1q61X24bRK9YUcwHMzKAli3Vbdv8eWdBdPP5deECsG+fddoCiV+D6B999BHq16+PIgr3fd68edOl7W3cuBHDhg1D48aNkZeXh3HjxiEpKQmHDh1CTEwMAGDq1Kn48MMPMW/ePFSvXh1vv/02OnbsiCNHjiBWcol58ODBmDRpUv7rQjZH/amnnsLZs2exYsUKAMCQIUPQt29f/PLLLy6lmfxDL2Oi2wYJtm0DGjbksByBynxesYJBnsjLE3vO3bhhvVxvDSQGqIKf3s459izWN9u61dtviz3NzZYvF3u1jR4tBs3N4uOB117z3n7NmEcFF3eD6M5Ig+jS5+yJTrbCw4GnntJu/xF3IzNbt4o9gyn43b5tvywyErjvPqBJE/HuBH+S6+CQng6cPy9erNRbvZHU27xZPl+xDaIDrueDckF0ubt5zOs9+mg4tm+3LA+0MtevQfRq1arhpZdewtO293vetWfPHjSS1sadMAe0zebOnYuEhATs2rULrVq1giAImDFjBsaNG4devXoBAObPn49SpUph0aJFGDp0aP5no6Ojkagwe8g///yDFStWYNu2bWjSpAkAYPbs2WjWrBmOHDli1fOd9EGvPdE7dADuvRf45x/fpoN8w1ww6LkCoee0kWjMGODDD+2X6+3iDAOawU9v5xyDovpmW7caN876ddOm1pNKeYvSecHzJbjIBZO8wZ0gOs+t0KR1mRihq4F2yR9++MF+mfQ88He7rlQpIDPT8tpoBGrVEod4WboUeOQR/6aHvOfwYeX3XM37fvsNWLRI7LEOWM5TadvxwQeBgQOBOXMsy8xl8Pbt1jsMtCC6X4uKRo0aYdeuXYrvGwwGCB58gxkZGQCA4sWLAwBOnDiB9PR0JCUl5a8TFRWF1q1bY8uWLVafXbhwIeLj41G7dm2MHj0aNyRdBLdu3Yq4uLj8ADoANG3aFHFxcXbbIX3QS0902yA64DgDI30zn1cMVJMnvvlGfrneziub69QUhAoW1DoF1njhRt/01shhoDN4mExA/frq1vWkJzqHcyFHtA6iO5pQUm91RPIO6USMZtLzoHBh/6UFAJ580vr1zZuWMdL//de/aSHvKlBAfnlkpOv5S3KyfHtWWi8rWFCcG0eqWjX5wjXQyly/Xu+cNm0ash3MkFCvXj2Y3KwRC4KAUaNG4cEHH0SdOnUAAOnp6QCAUtJ7Su++lk5g2qdPH1SuXBmJiYk4cOAAxo4di71792L16tX520lISLDbZ0JCQv4+bGVnZ1v9r5l3L+nl5uYiV+4+GReZt+GNbQWjvLwwAPaDvQqCEbm53mt12R6Hw4cjABgk75sgd62Kx817/PlbsJxXJuTmahntUa5lG43ePcfVYp7kCut8wsxozENurvu1CM+Ogf05FRfnm/zSXSxXPWE5vv36mRAbK2DlyjA884zRo3POW2kyy84O5mMQOJR+C0ZjOKR1Gn8dJ5PJer9mOTme5Zl6Ftz5kT2xF7qDCKKEwSAgN1eml4oCQTDA3OQVBMs5IwjybQUAyMsTyz89Hwe9l6nmbUkf9clSJzOZtM5TLOeqPdfOe7PAOAbBzdExsC1XAUAQcvOHVXnpJQOOHAnHY4+Z/NK+s80Xb97MhSVv1qaN6S2h/lswGOTzlyefzMWmTZbz0LXvRzw3BEGMjeTmWrZjNObe7RwjLdvlzx+jUevYikjt/+7XILrScCneMHz4cOzbtw+bN2+2e89gc2lFEASrZYMHD85/XqdOHVSrVg33338//v77bzRs2FB2G3LbkZoyZQpSUlLslq9atQrRXpyR0BzoJ2uHDlUBUNdu+b//HkFq6lGv7898HHJzrQeaunIlA4D9AOipqaleT0Oo88dv4dChewDUxrlzZ5Gautvn+1OmPFDi6dOnkZq6T/F9X2Oe5Fx2dicA9l2At2zZjAsXMjzevnvHwP6c+u8/755Ltz28V5/lqnvE3h2W43vu3BmMGLEHHTsCFy8C2hVH9ufc2rUbEBsbfMcgUNkeh/Pn7wdQNv+1v+oyV660ABBvt3zPnv2Ijz/tlzRoJVR+CzdvRgJIVrXunTt3kJqq/nvZvz8eQAsAwF9/bUdOzmUAwOnT9wGoLPuZw4et2wt6PA6BUqYC+vz+zEymrjAHDf/6awdycy9plpY9e0oCaC773u3bt5CaKjMroEp6PgahQu4YXLnSEkBxq2WbNq1F0aKWC1zTpomP/ihyjx+vDqBm/uvff18PQBzV4fDhw0hNPeb7RPhYqP4W9uwpC+B+q2WtWp1BWtrfyMxsBXPMyrW6nViXv3jxPFJTdyE9vQmARJvtWOr7Fy5clN3K2bPnkJr6twv79Q215apuR95yFKC2NWLECCxfvhybNm1CuXLl8pebg/bp6ekoXbp0/vKLFy/a9U6XatiwISIjI3H06FE0bNgQiYmJuHDhgt16ly5dUtzO2LFjMWrUqPzXmZmZKF++PJKSkhQnVnVFbm4uVq9ejY4dOyLS0b1fIerYMfn78e69twaSk6t5bT/OjkN0dFHZzyUnq2sokHP+/C0cPCieV+XLl0Nycmkna2ujQoUKSE4u53xFL2OepF5UlHzR++CDLdCggfvb9fYxKFfOu+dSpnSQRTewXHWP7RApFSuWR3JyGW0S48SGDR3RrVtq0B2DQKP0W1iwwLrXrr/qMh98IN9buE6dukhOruOXNPhbsOZHcjZtMmDAAPljLCcmppBL515srKU92axZE7RuLfY0Tk1VHrujWjWxvaDn46D3MhUIjPM4TDKGS9OmD6BtW+16ohcsqBz7KFw4xq08NxCOQbBzdAymTLHP+zp1ao8SJfyVOmv79lnni82atc1/Xq3avUhOru7vJHlNqP8Wrl61z1/KlCmL5ORETJ5sOQ9dyWf69DHh++8NSElJxIMPJuPzzx1vp2RJ+9E9pOnQmtpy1W9B9Jo1a+KNN97Ao48+igJKA/IAOHr0KD788ENUrFgRr732msNtCoKAESNG4Mcff8SGDRtQubJ1bwLzEC2rV69Gg7uRiZycHGzcuBHvvfee4nYPHjyI3Nzc/MB7s2bNkJGRgR07duCBBx4AAGzfvh0ZGRlo3lz+anFUVBSioqLslkdGRnr1R+vt7QULpTHtIiLCERmpvqKultJxyMuTrwzxmHmfP34L5vMqIiIMkZE6m5HvrrCwcJw+HY4RI4BnngEef9y/+2ee5JzSuG8FCkQ6HA9TLXeOwcSJ4p+UIHg3v/T0vGC56h15efrNv+bMiUS3bsF/DAKF7XGw7dvir2Ok1Kdm2bIIDB3qlyRoJhR+C++/D1y96nidyEjkD28wYIDBpe+kRg1xXHRBAGrUiMgvZx2Nfx0WZl3+6fE4BEqZ6qtteos0fylQIMIr9TB3FSqk/F5YmGvnvS09H4NQIXcM5NoEJUp4pz3gDtv95uVJF4Rj1KhwnD4NfPUVNAv0eypUfwtydanwcLFNIC0PXfluFiwAPv8cKFxYDCtLR+aW284vv0Tg3Dn7GegNBn20TdT+734Lon/yyScYM2YMhg0bhqSkJNx///0oU6YMChYsiGvXruHQoUPYvHkzDh06hOHDh+P55593us1hw4Zh0aJF+PnnnxEbG5s/PnlcXBwKFSoEg8GAF198Ee+88w6qVauGatWq4Z133kF0dDSeeuopAMDx48excOFCJCcnIz4+HocOHcLLL7+MBg0aoEUL8da/mjVronPnzhg8eDBmzZoFABgyZAi6du2KGjVq+OgbI08oBan8PWHMwYP+3R/5zq1bwNix4nM9T+5jMABvvQX8/rv45+8gOrlPywmt3nwTePppsQE3bRrw4Yec5DFY2E41c/myNumgwKfVxE8dOwJ//AFUrAhIpjRCWpo26SHvMk9a58jZs2Ig/OJF4N57Xdt+2bLAuXPi+SsdWdRRXS7QJjkj79B6YtEKFZTf03Pbg9xnW0fbvNnxBLO+ZvsbyMqyPD97FrgbCsOqVfaTkJK+OWrX9e8PbN8OPPSQa9s0GKwnv1UzveXff9uP5BFoZa7fgujt2rXDzp07sWXLFnz33XdYtGgRTp48iTt37iA+Ph4NGjRAv3798PTTT6No0aKqtvnZZ58BANq0aWO1fO7cuejfvz8A4NVXX8WdO3fw/PPP49q1a2jSpAlWrVqF2NhYAECBAgWwdu1afPTRR7h58ybKly+PLl26YMKECQiXTOe+cOFCjBw5EklJ4phQ3bt3x8yZMz37UshnlH6IrICQu1autDw/c0a7dDgjCPpOHynnQ1rmTwYDULWq+NwcZJg7F3juOaBxY+3SRZ6zrdDed5826bBVpw5w4IBrn9mxA1i+HHjppcDtARXItGrkjB0LtGwJVK4MVKpkWR6h20EpyRXOLth27gwk3L0D3N3fvYNRPGUFWoOevEPrIHqlStZ3XUixDRucbOtoKsNgPmP7G5COaCwNqIfo3JwBa9Mmse5sy5yvDBkC1KsH1LWfUtAljRsDa9YA5VwcDTTQylyvVT/PnDmDCRMm4KuvvnK4XvPmzRWHQHGVoOLbNhgMmDhxIiba3qd+V/ny5bFx40an2ylevDi++eYbV5NIGti1C/jgA/F5o0bAzZvAkSPia71UQARBP2khdW7csDyXViL0RhC07cFAzun9Ip/k+jGOH2cQPdBJG2hffgk88YR2aZH67Texsu7KsL7duok9Ue/csUy0Rf6jVSMnIgJo0wbIybFefvw4MGUKULs20L27JkkjL3AURH/rLWgyZI+a3nQUfLQOogPihe5du+yX66WOSN5lm9dofZxtfwM7dlie5+VZnjOPDCydOsnHL8znW1gY0KyZ5/sZP17Mw+6OgK1aoAXRvVZUXL16FfPnz/fW5ojc9vDDwN2RfVCrlvVwFloXTGYseAJPtmWSdF0fP0EAHEw7QTqmh8YbYB1E5wWZwCfNr/r0AWJitEuLVIUKQI8eyu+vXg2sX2+97OJF8XHnTp8lixzQupFjW7ZlZgKvvy7W+y5d0iZN5DlHdarx44GSJX2z3+oO5sfT+lwn/7h+3TqwpId6GOtdocU2/9P6HLTdvzQvlAbRmUcGFn91AIyOFjvrVKnin/1pRXVP9OXLlzt8/7///vM4MUTecO6c5bnBYH27r9YFk5nJZB2oIv0LlCC6yWRdAf/rL/GODL1cQCJ9DucixSB6cPnnH8tzvZU7Suf8vn3A3dHzcOyYZaghMzbetKHn7z0jw3fBVvItrebfGDxYDKJu22Y9ZB+g73OdvCMnB6hf33qZHtqJ5runKTTY5jVan4O2+791y/JcOoSLntvCpJ5e2p6BVuaqDqL36NEDBoPB4RAqBr0cBaK7DAbrH6XWBZMZC57AYjJZTxKr9fH7+Wex550c2+FcGjcGNmwAWrf2S9JIhUAazoV3NQS+q1ctz/V2UUTpnE9Ls7xx6ZJ9EJ20oedGjtblMrnv5k1t9hsTA0ycCLz/PoPooWj8eOuJigF9tBOVJtrVSx2RvEtvPdFtO1tIO2JwOJfgc3eaSM3JlbmZmeLwja7OaeIPqn+mpUuXxg8//ACTyST79/fff/synUSq2P4ADQaxd5KZUuDR31jwBJZXXrHMRg5of/wcjf0qNya6bSOB9EnrirMZe6IHF/MtnN4Y69BfpBNZyeW3DHBpQ8/fu9blMrlP6zqKXHCS51Pw+/FH+2V6qYfJYRA9OOktiG67/1WrLM85nEtw6dgRGD5c61SIbM+nvDygRg1xgtLz57VJkyOqf6aNGjVyGCh31kudyB9sJ50yGKx7uFSs6N/0KGHlPLB8+KH1a61uPVZDbkx0Z+kVBFaG9EAvDSQG0YOLOYhesKC26ZBje86Hhwv47bfKGDTIcqMkg+j6oefvnfWq4FKjBvD55/7Zl1zZq+dznbxDbngzrQOYjuiljkjepfcgupSjnuhsSwaeVauAe+/VOhUi23Pnxg1xjsO8PODff7VJkyOqh3N55ZVXcEs6KJKNe+65B+ttZ4By4Pjx45g7dy6OHz+Ojz76CAkJCVixYgXKly+P2rVrq94OkZTtpAkGg3WGrxds7AU2PR+/uXPtlzlK75EjQMuWQOXKwJYt+hszOZTopYHEIHpwMc/nEBWlbTrk2J7zggAsW1bNapmeL1qGGj00kMPC5Ms0PZfL5LrDh/23LwbRQ5NcfVcvASU5eqkjknfZll1aH2dHQXSlMdEvXRLn3ypYEPj7b6BwYd+lj7zj2We1ToE12zJX72Ww6mtdLVu2ROfOnRXfj4mJQWuVg+5u3LgRdevWxfbt27Fs2TLcvNtVeN++fZgwYYLaJBHZkbvdY+BAMVP3R2YxYYK6oBODAoFND431p58WKzqJic7XdXS+bd0qVn527BCv+JJ//d//iXfIVK4s3rKmBwyiBxc990S3ZTIZcOVKIZtl9uvpvXIdbLZtA2rWBH77TeuUKDfw9VAuU2CSm/tj/XoxoLpoESOXwco2iH7tmr7Lyb17xQBrrVrA/v1ap4a8Re8Ti0op9UTfvRs4cwY4etR6DHXSp6FDga++0joV1mx/B9LzS491fk1+pq+99hrefvttrF69GgUkNZe2bdti69atWiSJgsTRo9avDQageXOxYuSPzGLiRHESBGfY2Atsejh+X38tjvev5uKQoyC63gupYBcTI+Zb//6rn8Ybg+jBxVz26eX8klLT40oP+W2oGzvWvz2DHVG6W6p3b/2M7UmB5f777Zft2CHeqde/v+Wm7d9/B9q2Fd+jwCfNS37+GShaVLOkWJk8WXy87z759//5B3j3Xf+lh3wrUIdzkbYZpf8DOwrqX0yM1imwp6YnutEIPPkkMGyYf9LkiCY/0/3796Nnz552y0uWLIkrV65okCIKFkoZtz+DB2r2xaBAYNPD8TMYxNvlIlQMyuUovdL39PB/hZqwMDFQreY4+guD6MHl6lXxUY9Dm6nBnujaS0vTOgUWSkH0I0eATz7hHVXkOrXl72OPARs28GJNsJDmJXoa7mzsWLFX7/jxyutcu+a/9JBvBWoQffRoYPBg4M4d5YA66dPgwf7d39ChztcRBLGzxmOPAQsWWJ9T5ucHDgCLFwOffgpcvOibtKqlyc+0aNGiSJOpke/evRtly5bVIEUULGyDBHptaLOACWx6On5NmjhfZ+pU4M037SfeBRhE15rWlWU50mI4IUG7dJBnBAF47z3g2DHxtR56briDQXRtbNhQDmPGhOHqVe3HaJVylmdev+6XZFAQcXaxePHiGjAaAfPUYIcO+T5N5HvSwHmFCtqlw5bBIA7v52iOIpaBwSOQgujSMdFzcoAvvxQvLErPR/ZE1z9/35n68cfAhx86XkcQgHfeAZYuBfr1k49PSON80nNRC5r0fXvqqacwZswYLFmyBAaDASaTCX/++SdGjx6Nfv36aZEkChK2GbdWlYz69YE9e5TfZ7AysOmpgpCcLI6P6KhRd/o08NZbwIMPAklJ1u8xiK4trSvLclq0ANasAeLigPh4rVND7jpwAHjtNctrPd5VoCY4O3Uq0KCB9QUdBhB8KzcXmDGjEQCgfHl9BdGdTX69f7++Jwcka3r4LTvrib548b2IjLRUkPRYbpPrzPnaSy+Jcz7oTcmSyu9lZgLvvy9O5tiunf/SRN5n2/bSeqgNuTI2IkIMYMrFNrKzrfPQw4eBP/4Qg+z16wM9evgooeQ2f995ExkJvPgiULcu0L27gDt37CuVP/8stjvN5OIT0viL1nfXulwN2LRpE/JkUp2Xl4dNmzap2sbkyZNRoUIFlC1bFjdv3kStWrXQqlUrNG/eHOMd3btE5IRegug//ADMnAnMmCH/vjkzuHJFHK/29Gm/JY28QE/BZoMB6NhR3brmXlRSDKJrS4+NcYMBaN9efpxYChy3b1u/1uu55sy6deJtw1J6CLwFM2kPn2vX9BVEd3Ye9+7NoQ4CifS3/OCD4mPx4v5Ng5oLjAsWWE48Peal5DpzPte2rbbpUNKiBdC9u/x7W7YAr74q1tUosJnbXiNGAGvXikN1asnV/O3rr4HLly2vhwwBxo0DUlKAnj15d5geaTFHksEAdOgAjB+vHGzIyLA8l8YszL8RaZxi9mzLcJVacLka0LZtW1yVSXFGRgbaqiyFIiMjsXDhQvz777/4/vvv8c033+Dw4cNYsGABwp11MSFyQC9B9CpVxFvnX3hB/n1zOocPBwYO5FXaQNO4sdYpcI/597BzJ7B3r/icQXRtsTFOvmL7e9ZTINRVS5ZYv2YQ3bdsywU9nTt16zpf56WXfJ8O8g7pb3nGDOD118UJPP1JGkRXE1BnUzU4HD8uPurxLi1ArB8OGuR8PdbdA89ff1l6dZvzwAED9HFXgVx5/957yuv/+CPw3HPK79+44XmayLu0nANCbbv3zz8tz+WC6JMnA9WrA1lZ3kubK1wezkUQBBhkfl1XrlxBjIv3n1StWhVVq1Z1NQlEigJtTHRzQ2H3bu3SQq6pVw94912tU2FNbYDDZAIOHgQeeEB8ffo0g+haYxCdfMX2orIezzW1eZce0x7MpGWBIOgriD5zpthglzawbK1d67/0kGek9fRKlcSGsb9JhyJo0UIc49cR2/woK0usT1Wv7vWkkY8YjeLdwIB+g+iA2HPTYHDcnjUaWUYGksOHLZ2xTp60lLd6OYZy51rt2o4/4yiQqfXY1QScPWv9OhCC6NKLL+b2jG2c4soV8cK7s/HWfUF1EL1Xr14AAIPBgP79+yNK8u0bjUbs27cPzZs3V7WtAQMGOHz/q6++UpssIit66YnuzMGDQMWK6jOSjAxxjDRn4zaS782era8JiFxhMlkXpGlpDKJrTS+VZgo+epusSk6hQurWsw3iOivbs7PFhpvWt0UHKr33RJ8/H7jnHuV19Fr3I3vSY6XVeSYNonbp4noQ/eGHgVWrgJ9+Ep+T/l26ZHmuMnyhiUKFgOnTxfGEleTl6ftCAFmTtsPOn9dfED05GXjySeD77y1xleho97en9djVoe7sWeu4xejR2t5NpfY8P3fO8lyuJ7rZr79qE0RX/XONi4tDXFwcBEFAbGxs/uu4uDgkJiZiyJAh+Oabb1Rt69q1a1Z/Fy9exLp167Bs2TJc58BJ5IFACaKbr66paTAcOQKUKgW0bu3bNJE6gXwhIzfX+jchCAyia00vlWYKPoEwnMugQeLY+44CBIBraTcagTp1gLJlrcfpJPWkdSm9BdEB5pvBRA9BdGm9LiwMePppx+vbnn+rVomP8+d7N13kOx99JD6WLKn+Yq5WnOV3DFIGFtt2l96C6EWKAIsWWQ9J68lvhD3RtXX8uHU5O3asdmkB1AfwpUMIOQqia/W7UR0Omjt3LgCgUqVKGD16tMtDt0j9+OOPdstMJhOef/55VKlSxe3tEgVKEN1coKj54a9YIfaq27LFt2kidQI5iP7008CXX1peSytv5tfkX3oLTlHwCIThXOrUEedoyM1VnogbcK0nekYGcOyY+PzIESA+3uNkhhw9D+cCOD+X9Vr3I3t6CKJLe/EajUBcnOP1lYIAd+54L03kWxcvio+25aQeOftdMIgeWKR5ntGovyC6HE/avgyia8v2vNL6PHNn/+bfiFx+rVW9weV/Y8KECR4F0BUTEhaGl156CdOnT/f6til02FYk9BoUNKdTTUbCxqD2pA0sPU7j4EoBsmuX5Tl7omtP68oMBa9A6Ilu5ixttr8TR+WiHoJygU567hiN+vsemW8GD+nvVavjWqQI0LSpOPyTmrs+ldLJIHrgMAf2XntN23SowSB6cLFtd5nzQL2Vs9K82ZMgOs9PbdnWl7WeGNuTILqeeqK7vNsLFy6gb9++KFOmDCIiIhAeHm7154njx48jj7808kAw9kQnbWzYADRrJt4JUKaMuOzPPz0bF85XXKl4ZWdbntsG0Y8cESfVGjXK/nPr1onfx+rV7qeT7DEPIF8JhDHRzZylzZU8Tq/lfiDRexBd60YgeY/0XNPqPDMYxLs9r1wRJ15v397x+krn38aN3k8b+YY53BAIY4k7+12wp29gkdZR2rUDzCMZ662O5q0gOs9PbdnWibU+z4IliO7yT6J///44ffo03njjDZQuXRoGN2o8o2wiNIIgIC0tDb/99hueeeYZl7dHZBYoQfQhQ4A+ffTXMCWLQYPEccR697aMYV+ggLZpUtKli/pJNaRBdNvhXL79VmxIbtkCvPMOULCg5b1nnwVOnwb697ee7IM8o3VlhoJXIAXRnZWFrgznotdyP5BIz528PP3VVYoXF3sN37oFlC8vlk0UmPRy54jBYKnjOQusOspLjUZe5AkE5sBeIAzR6I2e6EYjMGxYGMqWBSZMsCz/9FNxTP/33weqVfMsnaSO7XBpABATI85/piddu4rD7DVu7Nnv5I03gDVrvJYscpH+guiWBEVGqrvI4iiIrtlcKq5+YPPmzfjjjz9Qv359t3e6e/duq9dhYWEoWbIkpk2bhgEDBri9XaJACaIDwM8/a5+RkbLjx8VHcwAdABITtUmLM+3aAW+9JVZUnHHUE136+8nKsg6im4MU5897llayxjyAfMW2PNRbIFTK1bRJy/Zly8QLf5MmiXcK6bncDxR6D6IXLCiW0Tk5QK9ejoPo330n3lk2frw42Szpi16C6FLOOkzYltsFC4p1JkD87TCIrn/B1BNdTRD9+PGimD1bPDHvvRdYuxZ45RVg2DDx/bJlgU8+8TChpIptILBQIeDoUXFYKT1p3x44fBgoV84yh4A71q4V56pxNtcE+Ybt+aZ1h0Bp+an2LoWg6Ilevnx5CB62UNavX+/R54mU/PGH9Ws9N6azs+UrRnv3AkuXij1/q1TR9/8QSmJixIqEXr3+OvDgg8CsWcDixcrrSYPoc+ZYj/EuPR+l65HvMIhOvhJIPdGdcZT2Rx4RH6tXF+/ykpaZLD/dI70Ak5enz8ZvQoL46Oy8fuIJ8TE2Fpg61bdpItcFQxBdmm7OLRMYzMGbYA6iL14sXtzp0wfIzbWctOY88do1y7rS5+bP3rkjtkXJu2zrJcnJQOnS2qTFmRo1xEdP79jIyfE8LeQevY2JXqKE5XnTpsC2bc4/o8cgusu7nTFjBl577TWcPHnSB8khcs+xY+Itaamp1sv13IAuWFD+h9+jB/D228DQoX5PEjnQpInWKXAsLAxo08YyfrsSaXD866/F347Z2rWW5+ZeVeRbgRzYJH0LpiC6bRBh716x19yOHZZl5mGmGET3nPTcOXwYKFpUs6Q4JXdeyx33S5d8nxZyXSAG0R1dVGIQPTCYA8/BMJzLnDn2d55duQI8+aQYBBdDNvYbMd9xa8v82QEDgBMn3EkxOWKbR8TEaJMOV3j6OzEaxeHXZs8G/v7bO2kidaRlrB7udujSRcCoUX9h3bo8q458jph/M7b5HBBAw7k8/vjjuH37NqpWrYro6GhE2lzCvXr1quznGjRooHr89L/56yIXde0qTopoS88N6MhI+caf+frUli1+TQ45ERWldQrUcXaF2baHuXlCGwC4fdvy3FEQXRDECbTi4oAGDVxOYsiT5kuBHNgkfbNtqOklQOXMPfdcwwcfxKJHD0sVVS7tw4dbv5brqaLnOoCeSb/DQ4eAkiXF5y1b2t/xpzW1eeiaNUBamn57/IWS3Fxg5UqgZk0gPt6yXC95lLPeycWLi5122rcX64bSfIZB9MAQTD3Rp04F7r8feOwxy7KbNy3PMzPly0Kl8vHWLcvzK1eAypXVp5Wcs/3eAyHP8DSIbjIBH30EjBsn/ubYM91/tm+3PNfDRcOICKBVq3N48MF6+OordZ8JijHRZ8yY4daOevTo4dbniNSQC6AD+mhAr1wJdOpkv1wQHDf+GFzTF+n44HrmLIgurRwDyreBOhrOZetWoG1b8fmlS9aNYJJnMgE7dwLFilkv5++cfMW2B1mgnGsNGlxEcnJh9OgB/PSTuExNJdlcuZaW+6dOiYHf7Gyx99P99wdG0ERr0oZKQoKl90+/fmLwads2oGJFbdJmS814wABw9iyQlATs3+/b9JBzs2eLYzEXKwb8959luV7yKGc90VeuFP/eeksca19KrqfchQtiJwUGI/UjkHqiq/ld7N9vHUSXzl9kNAKCYF+IKt0FIl0u7VxD3mEbCFTbG1dLnnYkMxqBb78Vn5svYF25Aly9ygltfenMGesySm/1X7Vlvrk9c+qU+9vwNpeLjmeeecatHU2QTgVN5Cd6CKInJckvP3XKcfocZQpZWeJYdbYBOfKdQA6iFyki9kQBgL/+sn5PaVKPTZuA++6Tf+/sWcvzixcZRFdjxgzg5Zftl+slaEDB5dIlYPRo62V66eXpTNGi4hU8aXBDze9EEMQ/6WiDffsCHTuKAbsffgD+9z/gs8+8m95gJG3km0yWCbbDw4HPPwcmTwZefFGTpNmpWdO6p5WUbR3rwAHfp4ec27xZfLx2zfpc00sepTZg9OmnYoDC0ZjoubniuMIZGWJAQ89z64QSc4cSvQWV5Kj5XQiCOMFy2bLiOde8ueU9o9EAk8l+I2fOWH/eTHoO37njRoLJIds8IhCCyEWKAI8/Lk7U7Y65c63LX0EQ25jnz4vLa9f2TjrJWlqa9Wu9lT9q28BvvSUOMTVqlPvb8Da3dnv8+HGMHz8eTz75JC7ena53xYoVOHjwoFcTR+QpvQ3B0bSp5fno0dY9Ba9csV7XnClIb3kSBLHwve8+8ZZkaQWIfEvr2azVcrUwUerF5+j2Quln5Hpdkb1//5VfrpegAQUXuXFM9X7B5q23gI4dTWjdWrxKJx12Q21P9ClTxAmWpc6dEwPogBgAJuek+f/168CGDeLzsDCgXj3g+++tgzRa6tLFfpk5IBQIt8mHInOPRMC6DqGX8rB6daBCBefrmS8uSdmec5mZYgAd4PjSenHnDrBrl/g8EHqiq/ldzJwp3h3Utat454NUXp58XqgwAq9VQJ1BdO+zvbgbCOcgAHjSH9b2Qnd2tuVuCV7c9h3bNrreJld3pV2ydavn2/Aml3e7ceNG1K1bF9u3b8eyZctw8+6gW/v27VPd29xoNOKDDz7AAw88gMTERBQvXtzqj8hTjz4KVKok9j7Tk+bNLcNg2JK79f7sWeC11yzLBEHshX70qFgA7dnjs6SSDUfDm+iJXE90R3c8KAXRlXqoA9aFstpb6UOd0hjzegkaUHCRjodqpvcg+vjxwG+/GVG4sJj5vPGG5b2aNZ1/3mQSx9uUW06ukebx0gk527Txe1KcctSTlBd59Uk6uZkee6KHhVkH+ps2PS+73s2b9nUl23NOWkdiXqS9ZcuA6GjLazVli9bU/C7M8xutWCG2f6WMRsj2RFcibTN88IHqj5FKtvlAINwNAXiWTtt8UVpHDZROaoFI+r2XLQu0a6ddWuS4UuYrDS2l1UgBLjepXnvtNbz99ttYvXo1CkjO+rZt22Kr0iUCGykpKfjwww/Ru3dvZGRkYNSoUejVqxfCwsIwceJEV5NEZOejj8SgtG2PNK0MGgSUKAEMHKicYQgCMH265XVYmFgZsl2HlXBtKF380BtnY6LbUgqCO5r0hT3RXedoolYib7Od+wDQT4BKrZIlga+/Fp+r6an1/vvyy/UwrFugkatnFCumn3HQpZQa4GfPcgxqvZLmRXrsiQ5Yp6VZM/kgOiDeLu9oYlHWl/RFenEEAMqX1yYdrvD0dyH2RHe8Eek5LH2u94vvgShQe6L7KojOOprvSL93PX7PruRtSnfFaBUXczlr3L9/P3r27Gm3vGTJkrhiOx6FgoULF2L27NkYPXo0IiIi8OSTT+LLL7/Em2++iW3btrmaJCI7eiuQZs8Wx46uVUu5QiII1mM9hYXZZwzNmgEpKZbXempwBLtAqUi62hNdqce52iA6e6Kro3QnA3/D5Atz5tgvC5Q8TMqcZk8qybzw7Dq570xvw+OZyQXRBQHYssV6cj1nbt0C2rcHHnhAHHrv1Ve9l0ZSpsee6IB1fuko7xR7+VpeOwqis76kvUKFrF8HQi9YT38XShOLKpGew3oMvAU62zxCbzELJZ6k0zaI/vvvlufMF31H7xduXWmXKPVED5ggetGiRZFmO0o9gN27d6Ns2bKqtpGeno66desCAAoXLoyMu4PFde3aFb/99purSSKyo8dbo8wZhaOe6Lbr22YMO3equ7UuKwvo0wd4+23X00nyAiUA5WpPdPO4kLYcDefCnlWuU+qJXrWqf9NBoeHudDVWAiUPkzLnZwyi+5dc4ESvk2sr1fdcPe47dwLr1omP27cr39lAnpPWg2fMkF+uNesgunIkkUH0wFK4sNYpcJ0/eqJLKfVKJ+8I1J7o8fGAK6Mub9gAJCSIz23zRWnbc906cW6THTs8TiLZ0HsbPaR6oj/11FMYM2YM0tPTYTAYYDKZ8Oeff2L06NHo16+fqm2UK1cuPxB/zz33YNWqVQCAnTt3IkqvXV0ooOi5QFLKMGwzAbkgutptrVkDLFokjinLSrt3BEoASi747U4lePJky1AKttgodJ3t0EyA2EtSOnkikbfIlR16ClCpZdsTvUQJ17exaZP30hMq5syxL/D0Wj2X60l64QLQt6/jz737LjB8uOW2ckcXjsl3pk3TOgXypHU+g0F9EN1oBBYsAF54QWz0cw4ZfZF2NKlXT7t0uELNJLeOrF9vwIcfNlS9vqPhicgzRqP9XU56jllIRUWJc7L9/LO69YsVs/zebIO50oDorFlAaiowZIh30kkiQbCeW0iPF8TkYitdu8qvG/BB9MmTJ6NChQooW7Ysbt68iVq1aqFVq1Zo3rw5xo8fr2obPXv2xNq1awEAL7zwAt544w1Uq1YN/fr1w4ABA1xNEpEdPRdISpmYXE90Zxme0vvS8XBZAfKOQAmit2/vvW0984z8cmllyNFVbvPEuBs2eC9NwSIiggF08h25YE2g5GFS5jRv2GA/9rBa0sm5yTlBAGbPtr+lSa890ZWGY3AUsDx3Dhg7FvjkE8sFTgY4/ScQLuh5MpxLv37A//2fOIklOx3oi7kMqVcPWL1a27So1aqVZ5+fNi0cOTmOG8Y3bojDhf75J3ui+9Lu3Qakp1sv03PMwlbx4uLQsmqEhakLopvt3etZ2sjav/+Kd9XpmVxdQOnuQr0N5+LyzzYyMhILFy7EpEmTsHv3bphMJjRo0ADVqlVz+tkZM2agX79+ePfdd/OXPfrooyhXrhy2bNmCe+65B927d3c1SRTi/v3XflkgFUhmthWVs2fFW5wckRZKy5eLGc9DD7EXgS8ESgAqOtq728vJsQ9SqG0Ujh8PzJ8v3hav91vKfK1AAetx5vU45BQFD/PvLS4OuDtiXkAErmxJ892332aD3h+UvmO99tR25+4E828CsAy1FeplFFmT5pdhYQKSkkxYtcq+InjggPVraTmfkWFdR9LrbyhUZGaKF84AoGdPcfLqQGBbdsfEyE8e7onffhP/3n/felgNtiG9Sy54HGgxi5IlxTv83n4buDuYhCxpEH3rVuv3lHoVk/fYBp31WH+Wa5co/R7275dfrlXdze2wUNWqVfHoo4+id+/eqgLoAJCSkoIyZcrg8ccfx6pVqyDcPZpNmzbFqFGjGEAntzz8sP0yV8eF9ielTEyuorJsmeNtmT9z7pz4PSQnA1euMIjuC4ESRLedNAnwrOCUq6g7GxM9KwtYulS8PQ/gOQjYHwMG0cmXzL/RQYMsywIlD5OSjl+7a5c+GwHBRim/vu8+/6ZDrYoVXf+MtAFv/n/ZS9h/AuGCnjTviYrKw2efGVGnjv16jz9u/XrxYunn2BNdT6TzHATCOajEl3Pp3LrFiUV9Se77DMT2QMuWwLPPOl4nLEy53qnUq9iZO3eAJUuAEyfc+3youHJFHFZMSo+/ZbnzQ+n3oNSrXtc90UeNGoW33noLMTExGDVqlMN1P/zwQ8X30tPTsXTpUsydOxcPPfQQypYti2effRb9+/dH5cqVXUs50V2HD1u/njJF38ECtcO5qGHOOK5etSzLzHQ8yRG5R8/nlJQvgujFilkvc9QoNBqBXr2sZ14n+99hIFaaKXCYL25Je3QESh4m1batpTd9lSr25T15n1J50aaNX5PhU3JBdLkLwoIQ2ME2ct899wDTpwPXrhlRp84VlC8PjBoFOBt1dMIEy/OwMAbR9UR653Iglodmvu4oxo5YviP3fcq12wKB0lBqZlFRyufq6dP2y9T8JidPFv8qVABOnXK+vtnVq8ChQ8D996v/TCDr3x/49VetU+GcK8O5KDl/3jtpcZWqIPru3buRe/cetN27d7u9s6ioKPTp0wd9+vTByZMnMXfuXHz99deYPHky2rRpg4EDB6JXr16cXJQ88sQTWqfAMUdBdDWTiUqZG33SW0RNJlaAfCFQKtwxMd7d3ubN9r8p6flmG3j46KMwBtBl2P7unVU+iTxhDtZIK6OBGAyMjASmTgWGDhXvuNJjT5pgo/QdP/aYf9PhjsRE2I03aysvT31PdAbRfSNQvtMXXwRyc01ITRV/FK6mOy/Puo6Une29tJHrrIfo0S4dnvJnEJ1lrnfJtclr1/Z/OrxBrh3TrRuQkCCWxZUqKZ+rJ0/aL1NzXpuHhZELwjvSsqUYRJ8yJQw1a7r22UAUCAF0QD4fdnWYrfR08Xfl7zxdVRB9/fr1ss89UalSJaSkpCAlJQVr1qzB3LlzMWjQIAwfPhxXrlzxyj4oNAXa2GJmOTlAjRrAP/+o/4xc449BdN8IlAp3sWLAyy8D06Z5Z3sXLtgv+/Zby/O8PPF8M9+a99prOh5LSUPsiU7+ZL47KdB7ogOWYRU2bQqc4FsgkwuavPGGOKGY3qnp6durF1C9uuW1o57oWjTMSL/cCaJLz8mUFGDIEO+midwTyGUJg+iBy/b73Lw5cM9FuXZMTAzw5ZeW166Un2rOa3djGocOiY+bNhlCIogeKKTn/rvvir+HgQOBDz5wbTvZ2f6/o8PlquGAAQNw48YNu+W3bt3CAGf3uCklIiwMBoMBgiDAxIgfeUjP46EDyhWSpCTXAuiApTCx7RnMILpn5s+3X6b380rqgw+A+vUtrxMT3d/Wiy/aLytb1vJ85UrgueeAkiUjsHdvPAwG1rhtBcsYiBQY3nxTHA8RsO4pFKgNtXbtLM/ZoPc9ue84UALJaiZu/OUX8TZzM2c90cn7AjUvcjXdubnAZ59ZXgfKRJahIFDyNDm2Qyx6G4cE9R3b77NFC23S4Q1yPdFt80hX2s5qOkGyTHafHr876fny0kti/axUKde3o8VdXi4XIfPnz8cdmSl179y5g6+//lr1dk6dOoWUlBRUrlwZSUlJOH/+PGbPno20tDRXk0RkRe890b2ZiSn1RFeqAPXsKfYm27bNe2kIRi+/bL8s0CrcBQtans+Y4X4vQunkWmYbNlief/klMGsWkJtrwIoVlQPue/IHud+8Hiszoa5gwQj06PGw7N0XgeSttyzPmzUDqlUDOnYEYmO1S5MnEhPdq1STe+SCJoFyEVlNEB2wrzMB8j3R//kHKF0aGDbM87RR4HO1fpOba33nntrzk3wjWIZzGT/et9tX6on+6qtiPeK773y7/2AWTBcl5ILotr8rZxOASqdEDA8H/vsPKFMGeOAB+QvbwfT9+Zse253S88WcP9vG8aSTdUvXk8rK8m661FBdhGRmZiIjIwOCIODGjRvIzMzM/7t27RpSU1ORkJDgcBtZWVlYuHAh2rdvj6pVq+LLL79Enz598O+//2LdunXo06cPCkojP0Ru0HtjT5qJ1anj2baUxkSXC6KbTMBPPwHXrgE//ODZfoNdZqb9skCrcI8fLxZEL78MdO9u6ZnqKkFQH0AIDzcFbA8zX5Kr9Pm6EUSuM5nEk7dfv3BdVjbdUayYOJnaqlWB2/sT8O4cAtWqib1dSJ7cua/3epWZ2ga2XBBdrsH+5ZfieJuffup52sgiUPNXV/PQ0aOtXzOIrh+BWh42b+773stKdzO//z5w8ybwzju+3X8wC9S8T46anuhPPul4G9K7wsLDxTHP09KAnTuBM2fs12cQPbjIxVakQfTvv7fvRCM3JJque6IXLVoUxYsXh8FgQPXq1VGsWLH8v/j4eAwYMADDnERaEhMTMXDgQBQrVgy//PILTp06hbfffhtVqlTx+B8hMguknuh79ojjoLtLqSe6tEeVNIgulwayJ5epB1oQvUsXICPD9XHFbBmN6gMImzeXQ16eey2T2bOB1q2D8y4Jud8bJxbVr/Xrw3Dtmtap8A69l4dqeXO++WPHgLFjvbe9YBOIQfS+fcXHkSPVrW9bZ8rNlW+YadG7KRQEah1UbeD16afll+fkeC8tFJpsg4gdOnh/H87GRGe+6L5gCgJLe5GbORrOxTwpqJQ0TyxXzvr7UZqnhNyjx3K3eXPxsWpVS3tF2m4xGu3jL3LtGl33RF+/fj3Wrl0LQRCwdOlSrFu3Lv9v8+bNOH36NMaNG+dwG2+++SbOnj2LpUuX4qGHHkJYoEWlKCDovbEnFR5uPeyGq154QcxgbMdElwui6zHz1KtgCKIDQHS0e5+rUMHyXM1Ebd4wZIg4cWAw9dBesgR4/HHgwAHLMnPwvF49bdJE6ngS7Ni+XQzqHTwIzJ0LPPWU81tavUnaiAmk8tAR24tO5cp5tr3Llz37fDALxDHR584FjhwRJ6ZSw7aO9Pff8uuxwe4bgVofdRREl95ZKjcMHsCe6FoL1N7nUuY8qXFj8fGpp7y/j0cftTyX+60Gw/eolWAqU+LjgWXLrJfZ/n/SgGeRIvbbkJ5L+/aJQwaZPfMMMHWq4+27KlDLHm/Q4//+8MNi+2jfPuvhXMzzh9Spoy6I7mmHQXeo7qPUunVrAMCJEydQvnx5twLgo0aNcvkzRK7yZo81X7DNxDxpnN64AezaBXz8sWWZySR/q3IwFdy+FixBdHft3CkGArt3Vw6ix8cDt2+Lf9508aJ3t6el3r3FR+lc3Pv3iw3p2rW1SROpM2GCOKHl44+7/tk+fYDjx4FTp4A//hCXxccD//d/3k2j1JEj4h0jvXuLZaC5V0aw9kRfsUK8pT0jw73t6bExoReBWFcIDweqV1e/vu0QeEoXzQLxuwgEgfr7cxQ8bNlSvGBerJhyvssgOnnKnCetXi3WM+65BxgwwLv7OHXKfn9SDKK7L1DzPiXx8davN22yfi3tyBEeLralHZWr6emW51u2iH9DhwJxceKyYPv+CKhUyfq1wSC2la9cAWrVsr+DQVq+VqgAnD4NyEzX6XMuh4UqVqyIsLAw3L59G4cPH8a+ffus/vxpypQpaNy4MWJjY5GQkIAePXrgyJEjVusIgoCJEyeiTJkyKFSoENq0aYODBw9arZOdnY0RI0YgPj4eMTEx6N69O86ePWu1zrVr19C3b1/ExcUhLi4Offv2xfXr1339L5KLvv8+8IIGnlZGTp4UK1NmSsO5sOBR5/Jl4NYt++WhFERPSAAaNXK8To0a3g+gB6vTpy3PS5dmAD0QfPEF8MQT8reTOnP8uPhoDqADwNWr3kmXkuefF4P0PXsCJUpYlpsbHoHOtid6kSLiZKnuYnmoLBR6HkqHa7KdR0aK54lvBOr36qgeOHIksHSp2OAfNEh+nUuXxAuepA1pPhao56A5r4qLAxo2dNzm/fZbYN48z/bnrDzIzhbrHkp385A1aVlj7mgTyGzrBl27Wr+WBtHDwtxrS0vHu/b0wnaw1WWCValSYgAdsD9npOfUjz8CX30FPPec/9Jm5vKpfOnSJXTt2hWxsbGoXbs2GjRoYPXnTxs3bsSwYcOwbds2rF69Gnl5eUhKSsItSQRs6tSp+PDDDzFz5kzs3LkTiYmJ6NixI25Iuga++OKL+PHHH7F48WJs3rwZN2/eRNeuXWGUtJ6feuop7NmzBytWrMCKFSuwZ88e9DUPwki64elEnf5gWyHxNEO37YlnNIqzW5vJ9URnIaJszBj55aESRFcqtGyFyvfhDdLfPL+3wPLjj8ChQ55vx9d57o4d4uOlS0Biovh82DDLLZGBTq7c9OS3xB7GyuSCJsFyMcZsyRLLc9uOB1I8T3wjUAOYjvLx6GjgkUfEDgbFiimvx6ajNrKyxKByoLPNkxwN2fbEE0CZMt7dH2D9Oxg6VBxaVDoEDFm7dQv47jsDrl+Psvo+pXeRByrbelhKivVr257otnmomrLA9s4xTwRq2RPKbPM46esKFYBnn/X9ZMtyXG6CvPjii7h27Rq2bduGQoUKYcWKFZg/fz6qVauG5cuX+yKNilasWIH+/fujdu3aqFevHubOnYvTp09j165dAMRe6DNmzMC4cePQq1cv1KlTB/Pnz8ft27exaNEiAEBGRgbmzJmDadOmoUOHDmjQoAG++eYb7N+/H2vWrAEA/PPPP1ixYgW+/PJLNGvWDM2aNcPs2bPx66+/2vV8J20FQnDY20F029uQTSZxfFDpa9v9elKImEzAhg2Azc0aQeO77+SXh0rw01w4Oft/g2WsZX+Q/t4CIY8ii8ceA5o29Xw7vj7utvNiAEC3br7dpz/dc4/1awbRfUeaX339dR4mTBCDMcGKPdH9L1C/V2k+ft991u9J60SO5jraudO7aSLnrl4Vh1nzVjtIC+bxpOvXt17urC7u6d3ZSj3Rd+4E0tKA+fPFZf6c9yXQvP020LdvBGbMaJhf1rRsKd71G+ikeV3x4vYdN6Tnn7s90c3Diubm8o4HTwRanmfmqCe6lm1ql0/ldevWYfr06WjcuDHCwsJQsWJFPP3005g6dSqmTJni9POZmZkwydRWjUYjMjMzXU2OlYy7XXKLFy8OQBy/PT09HUlJSfnrREVFoXXr1tiyZQsAYNeuXcjNzbVap0yZMqhTp07+Olu3bkVcXByaNGmSv07Tpk0RFxeXvw7pQyAEqLw5JjpgPVQEIDYGS5Wyfi23X3fNnQu0bSs2IAI1Q3ZEqUcag+jy65Fz0iIvVM6jYCId0z4zEzh61PVt+Pq4S4Poe/aIj8H0G+3Qwfo1g+i+Iz3fe/cWMHEiEBurWXJctnw50K+f+vUdBdF5nvhGoNYdpW2MRo3EuTPMpPmt3udmCjWdO9tPgBho5+Dy5cCQIcCkSdbLfR1El8sDDx4EHnhA/CPnFiwQH/fsScg/7wIhXqGG9KKO3LCFjnqiz5yp7nd46JBY954503p5oP2GtRao35dtXV964UbLIZxd3vWtW7eQcPfSWfHixXHp0iVUr14ddevWxd9OLg/9+OOPGDNmDPbs2YPo6Gir97Kzs9G4cWN88MEH6OZG9ylBEDBq1Cg8+OCDqHN3TI/0u7MTlJJGFO++PnV31oz09HQUKFAAxWzuvStVqlT+59PT0/P/Z6mEhIT8dWxlZ2cjWzKIk/kCQW5uLnK9MLOMeRve2Fbgi8x/lpeX69eJe9w5DoIQDvP1K/FzltfusJ25OicnDyZTOACxpMrOzkVWlthjwPxdmUxG5Oa61zrcuFFM77VrQE5OruZBQW//FozGCJi/OymTKQ+5uQFaAuWLdLpGeLgJubnGu1f+ldc3GExw5by9fVv8bdpk/XZpEwQBubkKs5nqUE6OeOGlUCG5d8X/yWgUYD6njEbf5VF6Lxc8TZevy1VH53tWVi4MBqBWrQicO2fAsmV56NpVKT+Q2474u/IVk8l+n4GYZymdww89BNiW9Z6UnYGWz/hTw4aWMjAvT/sy3lWdO4t/X3/tvLwDgLw8I7KzBcg1ifLyLOWcv/NVvefnnhAE+3rWxx+7Xy/1JelxMBoNMJ8nBoMJERECxHwIMJksZbv4m7E+/+67T8C+fQbUqeO9vEf/Zao+zuOdO+3zAqNRn+ebkubNxT/AfoLasLAImEz27RbxO7ecs+6wLiutv0fpHckFC7p2XmdmihebQuGCU1iYJb/LyDACiIDB4Ns6oX9Zzgv733kYzHmk0Zhrlfc/+mguPvhAvs0tlZwMlC0roHlzAdI6X3Z2rgudRSxtTPl0Bhu5+o8+6r2ulgnihTzL/9OgQR769QtD6dICoqNNXm9Tq02Xy7lqjRo1cOTIEVSqVAn169fHrFmzUKlSJXz++ecoXbq0w89+9tlnePXVV+0C6AAQHR2NMWPGYObMmW4F0YcPH459+/Zh8+bNdu8ZbC73CYJgt8yW7Tpy6zvazpQpU5BiOzAUgFWrVsn+/+5aLZ1RMmQ9nP9s06aNOHZMZlZIH3PlOFy92hKAeLdEamoqLl9uDaCoS/urX/8i9uyRvw9sy5btuHWrPoAYAMCGDZvwxBP34cAByz1W//13AqmpB2U/78i+ffFYsMAy8NRvv6Xqprejt34LRmN32eXbt29FZqaPZwf0OctvpUqV60hIuI1t26wHTLxx4zpSU//AzZuRAJIVt3TqVAYABwN/2rj33ju4cqUQPv10DYoWtYxBZLky/vDd/WciNXWD6u1qKS/PgGHD2uP27Uh8/vlqxMTYVk7E/0mcp6MwAGDFit/vNrx9R6/lwm0PZ6L1fbn6sOI7y5evAGDAuXPirElLlhxFWNi/+e+bTNLeEvbbOXv2DFJT93ghjUrs97lz5zbcuXPFh/v0HblzODo6GbdvixXpdevWIi2tFoAKbm0/NzcPqampniQxKJlMwNWrlnNpzRp95iVqPPlkdWzdWgZlytzEli1lFdc7fPhfZGdnALAft+n8+fMAygGAZueLXvNzT+TldYG0CfrTTz8DAPT8k1y9ejV2704EIN6VfPToeQjCdQBix61161YjNlZsfIv1Gus8+ZFHtmDfvhbIzLyB1NT1XkmT/stUC23PY/vy8ciRw0hNPaZBWrwvLKybbBA9NTUVx44VBdDa7W3funUbqalr7r5SriMJglF1Hnn6dCxefrk1ihTJwWefrUGBAvIXM6zrVYErK6sjAPH39Nxz4lWDo0dvIzV1rYap8p6CBbsgKysCsbE5SE393eq9//6rDqAmAGDDhrXIy0uCOWi+bt0q3LrVFubvxpFz5wxYssT6HP/tN1faU+K5e+nSZQBa50e+lZMTBsA+lpqTk2t3fLSk9hicPh0LoF3+67//3oZevcS2jS/qDGrLVZeD6C+++OLdSiUwYcIEdOrUCQsXLkSBAgUwz8kU0AcOHMCnn36q+H6rVq0wfvx4V5OEESNGYPny5di0aRPKlSuXvzzx7uxa6enpVgH+ixcv5vdOT0xMRE5ODq5du2bVG/3ixYtofveSb2JiIi5cuGC330uXLtn1cjcbO3YsRo0alf86MzMT5cuXR1JSEoqYBzbzQG5uLlavXo2OHTsiMlJdb5tQ0KZNa7uxU33JnePw7ruWqHNycjKGD3e9h0CfPiWQni4gPd2+0vTGGy1QrpylUHnwwVYYPtw6bVWrVkZyckWX97tzp3Vt5qGHkjW9lQbw/m9BriIKAC1aNEPTpoHVq9ORw4dj8Mknsdi2zXp5iRJFkZycbDdhra1r14q6tL8zZ8R8LyGhIzp0EL/HGzeAxo0jkJBg+V5jY4sgOVk5eK8naWnAhQviOXfPPUlQmls7Ojom/3mXLg/57MKT3ssFT4ds83W56ki7dp2tGnM1alRHcrJY2Pz4owEDB4YjJcWEESPkG4MVKpRHcrKHM3wpULpFs3nzpnjwwcDKsxydwwULRsBct23fvj3WrXP/hxQW9v/tnXe41MTXx797G5d6pV167yAgRaoICtJExIZUQRFFUUBFBbGADStiRVQEC2IBVPyJCCpFFPAFKSKISO9Ner0l7x9j7k6ySTZbk939fp7nPneTTJLZnXJmzpw5JyVm+ploop87uLUvsYNavK++WghWnherV6+JBg2M20np0t42G+364vb+PBSSkrxtt1u3XFe3RbkcGjdOxXPPifPnzpXFsGGlsGCBgvr1FfTseZXGVUHv3rmYMcMrNFq1Esr3AgUKh+37xoJMdWs9rlmzNrp2rel0NsJCSoonz2+0TNeuXfPcu6ksXpyNO+5IxsUXK5g1y7+GOj29ALp27YpRo6zTpqUl267XM2Z4kJWVjCNH8qNBg86oWtU3Tc+eyVi2zINly7IhqXZikoIFU3DokPbc3r2FXN3vBcJ77wEjRyp48cUkn++0dq233nTs2B6K4u0ku3btiNGjg1cidOrUJeCdDCVKlAAQ22Mbf5jFJyhcONUVdS5QmfDXX9rjVq1aoE2byM1t7MrVgGtu37598z43atQI27dvx19//YWKFSvmVUwzjh49imyjXv4/srKycPToUdt5URQF9957L7788kssWrQIVapU0VyvUqUKSpcujQULFqDRf9qNCxcuYPHixXj++ecBAE2aNEFqaioWLFiAnj17AgD27duH9evX44X//GS0bNkSx48fx2+//YZm/zkAW7FiBY4fP56naNeTL18+5DNo2ampqWFttOF+XqyTlpYKJ36OYMshNTU1KJ+bKSnJlr6tdu/2CqnkZN98JSUlIzU1cAWEXmGenOzM721EJNtC6dLAxRenuOa7hgPxexmdT0JqapLfgYk8EAoM8TtOmgTcfbc4s3Wr/CxPzPRpcntISTFvC/LCTFpaasQta9wqF0LNU7TkqhG5uama8k5N9fahjz8OnDoFPPBAMu6/37hfTU4W7SoSHD5sfD4tLXb7LKMylf1tpqWlhrSAm5sbO/1MNNEP0d3alwSCv3oybpzVWMjbZp36HeKhDPTI49e0tMj1jeEkNTUVFSp4y+H06SRcckkSdu0ChGWl9jt88gnw22/Ali3iOF8+URFzcsLX98SSTHVbPU5ODm4e5EaqVRN+ygGgY0fghx+Au+4Sv7ne1WDp0in/KaU8tvxyK4qorxMmWKdLTrZfr2VDErOx81dfif9z56Zi6FBbj3UtZmN+N7WHUOjbV/wZqRVl+Zsvn1bnUaBAakh+us10EGfPijg6pUsDM2dq/bAnJ4sDt/VH4URvqNWihZBFY8a4a9xrtwz0IipfvsjObez+RrZHLWfOnMHQoUNRrlw5ZGZmok+fPjh8+DAKFCiAxo0b+1WgA0DlypWxcuVK0+srV65EpUr2rWOHDh2Kjz/+GJ988gkKFy6M/fv3Y//+/Th79iwA4YJlxIgRePbZZ/Hll19i/fr1GDhwIAoUKIA+ffoAADIyMjBo0CA88MAD+PHHH7F69Wr069cP9evXR4f/IlnVqVMHnTt3xuDBg7F8+XIsX74cgwcPRrdu3VCrVi3b+SWRJxYCdegFRjACJJDvGcnAWLEapCIQZs0SwVuL2vdcEjMYDezsBhb1V68WLzY+rwZufewx6/tjAbv1X7YKiIU+ivhy4YK2zqvluGoVsGmT//sjWe5mu0bc4morEhQrxsCikeC/4XNcEUrbs6onOTlAr17CX3+IBsEJhyw7nd7NGCznzgWWXv2eZsHrSXSJp/nLd5KHhjvvFIv6ahDGijqPZ/K4YOBA/8+2KyvV5yoKcOutQolpsIk/L43d58dDe4kHlzThQD8mDXWMqtadu+8GbrzRawSwdi3w668imPDRo9r6Fk/t3gx9m3r9ddEn3HOPM/kJFX37cUt7sp2NJ554AtOmTcPVV1+NXr16YcGCBbjrrrsCetn111+PMWPGGLpG2b9/Px599FHccMMNtp83adIkHD9+HO3atUOZMmXy/j777LO8NA899BBGjBiBu+++G02bNsWePXswf/58FC5cOC/NK6+8gh49eqBnz55o3bo1ChQogG+++QbJUuuePn066tevj44dO6Jjx45o0KABPlLDLRPXEAsKKn0HbjWAMNnoEND3nDjRflp/6N+bCMKoXj3ErDWnnlGjxP+RI8V/WRBVqCD+33ab+O+vjvkre+NAm95Bjtn1WKpTwQzMYqGPIr6sXes7mXv0UaBpU+05MyVkJAd9P/0U/Xc6SalSok8O5ftJsfSIhAhAHl+Y1RM71o1W47O//gI++wyYNw9YsiS4vCUqsaxEHzBA/A9UIaFOKeNBKRgPxNJY0x+FCnk/Z2drx9dFigDffefdYiQrLu0oMe3+TkeOAH36AC+9BEybBvz4I2Dm8ljuV/09Px7KKZHH/fJ3N1KGhlK+H34o6t2kScLg7Y8/xHm5j83NNX/H+PFA797miz2xin7ckpJiPueOBfT9lFvmNraHLrNnz8aUKVPQq1cvAEC/fv3QunVr5OTkaJTNVowaNQpff/01atSogX79+qFWrVrweDzYuHEjpk+fjgoVKmCUquWxgWKj5Xk8HowdOxZjx441TZOeno7XX38dr7/+ummaYsWK4eOPP7adNxId9FXALQ3LiiuuENtq6og4G5aTtHffFUpcPR6PfcEzdarvOfXePXuAF18UQqR5c//P0g8E4t2a74EHgHjabPL000DPnkCDBuJYbi+zZwNpaUD9+r7XjPBX9mYTYzXodalSwO7d/vPsZhLNuiGR6dZNWJ2rPPKIcboXXzQ+f/y46E86dwauuiq8eVu0yPh8vFqiqwqDUOX9+fO+20QTHXUSGk+YKTDsKG+t+nXZ9Q0Vo4Eh/66xZqTwzjvA8OHAJZcEdh+V6CRSFPSG3TFcyM/I8H4OVImem4u8OAD+mDFD/KmY1fVALNHjYWydyEp01dOyqsht0ABYt857PZTyvftu4PrrvcdqfZPrlJkS/fRp7zi+devYtdI2Qt+mYk3G6nGrJbptJfquXbvQpk2bvONmzZohJSUFe/fuRQXVhNEPhQsXxi+//ILRo0fjs88+y/N/XrRoUfTr1w/PPvusxkKcEH9s2KA9jgVBNXYs0LIlcOml4thqAGHWtAJRohuh3jtmDPDBB8DnnwN79wJTpgi3BUOG2Pst42FwI7Njh/a4ZnzEHMojORma4JdyGRcoANSt6z22o0Rv2RJYtkyk1ddjs8H5hQvifzxMJAOxpiGxz2uv+U9j5rHu00/F/1deid7io1sGmuHGrsspf2RnU4mukp0trLlWrHA6J+HHaCxTt669+mPVr+ut3Yg9Vq3yLqYDsWeJnpYG0yDiVqj9lkVoMEKCIi3N+9koCGdSkrcjk8fmdvrA/fuB0aODy5dZvxiIAUo8jK0vusjpHDjHTTeJ71+lilDmli6tVaK3aweEYqNqtCAjy+acHON6KPfD8bYzMd6U6PIiIOCeuY3toUtOTg7S5F4aQEpKimWgUCMyMjLw1ltv4c0338Thw4ehKApKliwJTyxoP4nrOH1aexwL1Sg9Hbj2Wu+x0QDhiSeExbrZmlK4lOjLl4v/+/aJBYnbbxfHTZoAu3YJi79Onfw/J144eVJ7HAv1KRRkQRToSm9urlAM/vCDWHx48kntdbOJ8Y8/AsWLx4cS3WoiEG9tg5j7Hpf55hvr65GoF2ZtyS0DzXCjfq9Qv59+onHhAvDFF2JXVvXqoT071vjf/4Bhw5zORWTQy/GSJYGvvwbeftv/vVbKcS6iBkfXrtrjWFOiBwt9oruLeGuz//d/IojtlVf6XitSxPtZtlqP9G41s/5TbymsJ952eZYs6XQOnCM5WezAVKlXD5g/33v8yivif7CKdLn+7NkDTJ6sreNmlujxvPCtj0kmt/9Y5KKLgGeeEYafgHvmNraHLoqiYODAgZoo3ufOncOQIUNQUKqts2fPtvW8I0eOYMeOHfB4PEhOTkbx4sUDyDYhAv0aTiwqPY068u7dgcaNze/RK9GnTAGaNRPBXCxi9+Yxf74YyMuTl2PHtNfVwI87dngD0wTizz0W0dcnt3TUkSIUJbqiiHpx223CB6Ies8H51Knir0CBwPLqRqwG+vHWNojvIptbMFPKxKs7l3Ap0dU2u3OnWFBet04M1GvVEv6uE4mdO53OQeTQjwsHDhSLJHYCQxr14xs2CB+qso/R334TFqDNmoWU1YTg4EHtcbVqzuQjGsh1j+5cnMFsLFamTHTzEWmaNvWN0aJSvTowdOhqtG5dHxkZ3olfpOc4Zgpwf+5c4k2Jvnat0zlwD6NHC8viyy8XxyVKiB3xzZoFt5Av14++fX3dGZkp0eOhXuk5cwb46ittvJePPxbW/7FOx45eJbpb5ja2u88BAwYgMzMTGRkZeX/9+vVD2bJlNef88eeff+Lyyy9HqVKl0Lx5czRr1gyZmZm48sorsWnTppC+TKJx8GB+HDnidC6cRT8YjRclur/voVei33YbcPHF3mjs/vjzT+G3TlaiywpkeZLz77/meY03ISRvMQZisz4FgjyA1n9Xf989WJ/oKmfOGJ+PpTpFJXp8kj+/grJlfc/v2ROe5//5Z/i2jx47BmzebHwtXhcB1QF0qP3ziRPA778Ly/ObbxYKdADYtMmegpXEBmbtoGpV//fqrc1XrBCWdFdeKQKKqjz/vKhHnMYERp8+wJ13Op2LyCGPC+jOxRnMfu//QrwlDFddtRM33qgdqEZ6jJCbK3Y1b92qPW80dlYUsZB97Fh8KdGPHYv9+E/hpGRJ7257laQk4N57g3ueLKON4gHYsUSPl7n+I4+IhQQZ/XGsIusU3DK3sW2JPtUoOmGA7N+/H23btkXJkiUxYcIE1K5dG4qiYMOGDXj33XfRpk0brF+/HpmZmSG/K97ZsQO4446OKF5cwf79ibMdUk8iKNGnThUW5jJmVrxmVghGbNlirkQ3U5xTiR5fWFmi+/vuctkbCbRE6JOoRI9fZB+ChQoBp06Fzzr54ouFS6+vvvKeO3lS1BkbtggaGjUCtm83vhavbbByZfE/VPnTtq3v5F7lhhuAb78N7fmxRDzLOrPvZkeJLtexzz4TQdhVtm3zTb9njzcY+ZEjwlo9HnZdRYoJE2J/q7kVmZlirA3QEt0pjH7v0aPZLgHfsXtGBnDjjWJ3czjYu1fI69xcsYOnTh1x3mheOWOGUPiVL6/tW8+fF670Ah0buYVDh5zOQXzjbxyYm+t/t8Phw+HNk1P88ovTOYgcblSiRzUbr7zyCipVqoTVq1dj+PDh6NSpEzp37oz7778fv//+OypUqIBXVOdIxJLNm8Ws4MgRj49f8EQiHpToRgJA/h4DB2qvdegA9Ohh/KzkZO2EpEsX8/dmZ2u3xMhK9OnTjfOnF0TxpiikOxf7yGV/3XVA69bayuCW7VZGhMsSzMonLifKsc1994kJX5MmwOuvh//5X3/t/XzokNhaXqpUYBZLimKuQL/55vjz6/3RR0Dr1l6LcbnN3XVX4M8zU6ADwNy5iWUxGotjJ7uY7bKyI6PkOvb++9prRrup1H5/zRrRnqtW5a4GK+K53gGir2rSROwSpRLdmJycyPa1sv9llXgzAAoWfR+YlgaMHRu+569f7x0nb9rkrftGBihqUOvdu7Vt5NFHxfho167w5Sua6I2zSHjx15/asUQfPx5Ytiy8+XKCSpWczkHkSHgl+oIFC/Dwww8jPT3d51r+/Pnx4IMP4vvvv49mlmIWuQIl0kRPT7z6RLf6HgsWiBV5s0FgoULez1YR1Z9+Wkz0VMzqkZWiMN4Goolmid6mDVC7NtC+vdfvvV1KlPB+rlIFWLgwB127erVSbrWCnTlTtJEXXgj9WbREj088HmD4cGE5tXKl70JmuPnnHxEk+/z5wNxBWPl6/fRT9ww0w0W/fsDSpUD9+uJYbnNPPRX+9xUp4o0NEu/Es6wzMzSxI6OsduIZKdHVNH/8ISb3Bw4A+/fby2ciEs/1DhAuC1auFD5qU1NFnxLPlveBcvq08Ilfpkz43KXpMXJ35mYjj2iiHyOkpoZ33DBrlvfzddcBFSqIHTr+fKLrFaNnzwKrVoUvX9GESvTwI8ceadHCOq1dn+jr1oWeL6eJ53mn3Ge7ZW4T1Wxs3boVjS2iJTZt2hRbrUyDiCGJrESPB0v0YHyiA+YK7EAir1+44P1stpprNdiJNyV6olmiV6gAbNwI/PCD/UnFG2+IbbATJ/peMwqiFQr9+4s82gmWa5cnnxTKyocfDv1ZVKLHJ0b92j33ROd9iiK2UleoYGxBJ2PWZ4fL37rbkX+3SCzanT0rFpvtsH69WEx84onw5yMaxOLYyS6yezrA+10DVaLr25uVJbpcN2l57EUfYC+e652ecuWEW4oDB5zOiXv4+2/hnvTwYa1RTzgxkuf9+kXmXbGGfpy+d29kFxj27RPKSrlfXb9euHx57TXvOaM+85NPxLjof/+LXP4igZkSffLk6OYjnpB3hekDVesxUqLv2lUYNWtqBwDxIKfjed5ZqRJQt64w/HOLxX1UVUQnT55EEYsl+MKFC+PUqVNRzFHsIjd2KtG9xOKAPBAlet263s9mCmx5whiIYqF7d+PzVpZY8dZhJ5olejAMHSoC8t18s++1Ro0OIilJQePGwS9AHDokfBWPHSuiiu/eLXwlhgu5vzx+PLRnWSnR42FARrxEckFNv9vn9ttFvX/wQfv3yeiVhvFKpJXogTBxonCt8+STzuYjWOJZ1pmNbeQ6o4ZiKllSm0ZuY4sWaa/pjwHg6quBhQu19+nH6CtXAo0bx25dCYXFi7XH8VzviH+iEUBSLyefekooYojvuKZXL6BYMW/ckUhw6pS2rL//XiykyDz6qO99X3whxkXBuG5zEiMlepMmwB13RD8v8YIcs8gfRj7Rv/uuMs6d0wofoznb//4HXHqpsax3I/Gmk5FJSxM7/P78E8iXz+ncCKJuZ3ny5EmcOHHC9E+JN9PWCKEfoB8/LgIevfmmc3lygnhw5+LPJ7qM7AnJrKmMG+f9HA6LAqtBbrw110SzRA8Ws3rVtOkB7NqVjRUrQlOiL1mircdm2z0HDwZuuSUwn7Nq0Dcg9CAsVm3DyEqxUaPQ3kecw05fevHFwT3bbKHSn4UNF2q8BDKpCpbly4VCdvly32snT0b+/ZEkFsdOdmnaVOsqwsgS/f77hZuBMWO09wYzxrn9dmsL9okTgdWrY3fXQijoZXk81zvin2go0fXPjYasiBWMfKKnpoog6tu3izgkMuH47U6f9u/ORbZK17N7t+irb7rJ3o7SpUuBzp2FK0cnMFKi9+kT/XzEAkOG2EsXqBJd3wecP+87oDcySB04UCx6Dx4sjg8eFPVu6lT7748m8axEB4RewU16mahmRVEU1KxZE0WLFjX8qyVrN4glekv06dOFD9RIbjl3I/rJiZsal12MOj2z75E/v//nyRPDcFjnWVmix5sS/Z9/tMec4AVOyZKi3oXztzN61rp1wHvviUH+r7/af5bsyz3UAYdZvICcHOCaa7Rpa9QAfv45tPeR6GBU3+wo0YPtb83qkUH4GNP7EhH5t4qGj9tbbwW++SbyPvJJ+ClWzPecXGdSU0UafT0Kpo1t3QqMGOE91k/OY33BJRSoRCcy/pSp4UD/3FicJ0aK1q21x2r/ly+fcJkgx9gCwuOCZOVK60VGO3z3nVCKv/CCcAdz111iPmDEvfcKa/eePYPLb6BMnQrceac3FobsNlXFLZa0buONN+ylC1WJnpLi29kY1cMjR8R/VTcwaZKod7fdZv/90STR5wTRJqobYBcuXBjN18U1eiX60aPhf8e//4oOrXt34JJLwv/8cBAP7ly6dwe++kp7zo4leteuYuFEH2ZAngSG2xI93t25bN+uPY7F+hROMjKCd3kSzomKUTnIA1OrwD2zZokAkSNGAIULa/uMUBeBzKyofv5ZWBrKNGyojVdAYgs7fWmwVlpm9cifEj3RLdHl3yoaipG//hL/jYK/xrqsiPX8+8Po+8mLXmlp4r+Rj+BgkBXl+nYab8YHVmRlCcv74sWFqwh9AOB4r3fEGics0VnnvFx1FTB3rphPAr6/jdxH1q8fnjnl3r3Ct7lKMC5p5TH/22+LP0AscOuNGTZsEP+j0e9mZXkVrBUrip1NRvMTf2O7RMWsfnXrpvWFH0g9NHLnkpzsWxnsjKfl3aHHjgGvvy7aTpMm9vMTSRJ9ThBtoqpEb9u2rd80hw4dikJOYh+5oUQq8vOTTwKvvgp88AGwZUtk3hEq8aBEf+cdr9XqoEHiv/57pKUJpaHsLuCNN8QAqEMHbVpZuITbEj3e3bnoBzaJbrHy669CYfT448LaIxAirUS3qpcqZ88CN94oPpcp47vNPlJKdKPQHolel2IJo3phZ9Ae7ATTzCrLn7VSvC1iBoo8EZf7iCFDvJNqme++E249br898nmLNWJx7BQI8vczcudipkTX704LBr2SKN7GTVYsXAg89JD4fPCgUDzIxHu9I9bQEt1ZPB7g8su1xzJyH5mUFJ72mpamLfdglOhymR4+7P2ck+M77zWyBA83u3eLxYjmzb3nVN2MkZEjlejmNG4M/P6797hVK+Cyy0JToutlblKSsRL96FHhe//qq0UgaD3ye597Dnj+eTHW3LBBxPAqUgTo29e5PibR5wTRxhWiRFEUzJ07F9dffz3Kly/vdHZiAr1PdDPBdvy42OI0aZJ3W4pd1I0DW7cGl8doEA8+0UuWFCvXciAX/fdYtAgYPx547DHvueLFxX0VK2rThtudi9UgN94mg9xqrKVuXeC667xBmAL5PSLtzkUuq+PHgTlzxCBr9myvj3TZV7pqUR/OQYaZEj3R602sUrKkKMTGjX07NqNBe82a2uNwuHORXRNlZIiB/XffiYA6ehLd6uSee8R2bv0mx6ZNfbehA0JWDhoU+I6Qb76JzG4/NxHvfZbcfosWFf/l9qruIonE75DI7fTECe/nnTt9r8d7vSPWOGGJTiW6FnkHnb49yv2mxxOe9qq3DF61KvBnyIFIz5/XPjsQfvnFa6keCrfcIly4tGrlPafWu2XLfNPTnYs5n34KvPSS9pw+mGwgbdiuO5fsbGD0aFGOt94qfOnLbN+ufa8aG2fvXmGRfs89oh44GYSUSvToElVLdD1bt27F+++/jw8++ACnTp3C1VdfjU8//dTJLMUMencuZtx9N/DJJ+Lz118D8+bZf0ekLNzDid7iMxxKY6eQ864fqLRsKf7sEG53LlY+0detA+Jp3YuDbWOeew4oXRro0cP+PdG0RL/7buF+SuW550TAIbmfVJ9h152LoghriPLlgVKlzNPoP585YxywNN4WnOKRmTNz8PzzO/HMMxWgtzEw6kszM4G///YeByJ/ZEsks2CiNWqIhSG13Z05o42LkegD5owM4MEHfc8XKWKcXu2TAu2buncXi4nxTLwrM5OTxVh41SrvdnsjS/RI/A7BWFrGC7LcO3vW93q81ztiDS3Rncdq7hkJS3S9UnPfvsCfIY/3ZSW6fpxtZYX+11/CwjkpSfRNqgwIBnUh/8wZ7zl1rlG4sG96o0V+IqhRA3jgAWDkSO+5IkWA4cOFdwQgsDackwNs3Og9VhRjdy6//ebVkS1YIP5kjh41f688bt+1y37ewk2izwmiTdRVjufOncPMmTPx3nvvYfny5bjqqquwb98+rFmzBhfLviqIJbIiaN8+rcVlVpZ3Zfnzz73nv/8+sHe4WYl+4oToLF54wXvu+edjWzBZDWQCIdzuXKwsRa6+Gpg/X7iViQfoO9GYatXESnsghFuJvnevUGar9Vu2RJEH1ICwGDh3TizyyM8A7LtzmTlTBCIqWhQ4dMhYiWr0rN69hVW8VVriTlq2VHDHHX+gXr0KPteM6rO+fw2kv83IEHLs7Fnzrc27d2t9Mp86pVWiJ7KFqxFvvSViEVxzjXHbVvuAYPqmL7+0vq4GETNj926xPditMiURFvl69xZ/KrLSRLUM9Fc+RYsK/6xffKEdd1sRKZ/oZ88Cp09rg2W7DVnuUYlO9ETDEp07TK2R5aF+l5Y87g2XEl1RQi9reZwkK8r1z5UN7fS7tlVlZ26u6Js8HuHuTd4Vruf4cdHvmxnWyKj9vlr/OnfOxfHj+1GnTmm0a8eVnEAZPlyMs668MjADwdWrhYs/ldxcYyX6t99aPyc3V9tW5Lom68uCjSMWKlu3Rsd1EfES1VZ89913o2zZsnjzzTdx0003Yc+ePfjmm2/g8XiQxKXhgJAHBd27a4P1yD6yzQTehQv+lTputZ45exaoWlUIRFVpMWKE1+9irKLfNheO50TaEh0AfvghPO+QrQmcgpbo4SOcE5VvvhEKqH79xPGbbwL9+5unP31a+NVr3957TlFEn3b6tPacGeoWz6NHzQcmRhNAIwU6QCV6rGPUl4aiRD95UkzWypTRBqGV5e733wPvvus91tdX1iktd90lYoyY+RsNRYluxaJFwOLF5tdffln4b3/iifC+N5wkghJdT7VqwIABQJcuXlnhT2799hvw4YfCDZ9dIuETXVGARo2AsmXdG7MI0PZRRosOVGgmNtGwROe43j/jxgFt2wJ33KE9rzfuioQ7l2CQ+1Qrdy6yclM/PpO/S06O0J1UqaIdc8mcPCn0DmXKAH/+aT+Pav2rU0fBww//H95+O4c+0QNA/f2qVBGuXu64I7A2vGKF7/NSUwOvgHaV6B98EPCjQ2biRDGekd1BksgTVVHyzjvv4K677sL8+fMxdOhQFC9ePJqvjyusLNCWLPF+NhJ4hw8LFwWXXmotyNxqib5/v/DvfvKkd/LQvbuzeQoH4bJElzv5SPtEB0IfVCkK0Lq1mAz6s+aLNLREDx9mg5wyZQJ/lroVT/X2JW/zM+L4ce32PUBss6xZUyjkVewqM8z620CsqBJRSRVPGCnR9UGdA+lvz5wRCzSKog3aq5e7soJdX4fketm8uYh/UrmyCDpNfImUEv2jj6yvqwv8srGD20jEBRmPB5g2TQSEU/2k+6N69cDfEwlLdEURQb+zsnyVBG6ClujECics0alE9+Xxx8VicJ062vOxYIlu152L1Ttzc726k5kzjdPs2iV2DyqK7/zCCL0lOvu68BFIG546VXusKMGNd+wq0e3sUgg306ZF/50kykr0Dz/8EL/99hvKlCmDm2++Gf/73/+Q7VZzZ5djtwMw6rR//124J/j9d1+f4jJuVaIbKbTiYVU3XEp0+d5wW6JbbZH3x6OPCkW5rBQCRD1bvly45DDyJR1NuO0zfBj9dhMnat1TBMP58/630RtZg//1F7Btm/ac1aBazr9Zf2s0AczMNE6biEqqeMKoL7WacAaCXJ+thkRmlugFC4o+dNAgUceHDg0uH/GO2qaj3a/HQts36gu7dNnmezLOsaob11zj/RyIEiiQNm2XaFjwhgMq0YkVTliis87ZJxI+0XftAkaNCu0Zsh7ArhJdX7/ktEZ6BUUBunYVhj/r1mnvt+NKj0r0yBHKQpjwyR94YeiV6LKOTP783Xe+ivtIw7rlDFFVovfp0wcLFizA+vXrUbt2bQwdOhRlypRBbm4uNoQjPHICEYoSXcZMUa4o2kAJbsJIeMVDpOtIWKKHw+LCziD3xx+FQun5582f88wzwn+/Puq2myx05bwULAgwTEPwGNW9cCzqyFa7gaAPEgMAN94otrDKLl6MCMQSPSPDf1oSexjVXX0dD3bnjzwJDEThptZLWtb5Iv9W5cqJ3XflyonjcP9e8TCB0detffuycOed64wTxzFWZRls++7d29ziNhxKdDfLFnnMuHSp7/V4aDskeOzGqAmWf/4Bxo/XnqO8tE8kLNHD4XJCHpPrrc1HjwZatAAOHNDqOHbsEK679G5W9M9Tv+OpU0Ihun+/+C+nsaODUdOr72G9Cx+h/JYLFyYF1deIgKTeYzMlOuANXh4t9O2yWTPx//77o5uPRMORJl25cmWMGzcO27dvx0cffYQbbrgB/fr1Q/ny5TFs2DAnshRz+FsFnTxZ/DcSePK9ZhP2I0eCy1c0GD7c91w8WKKXLy+CxmVkmFuz2kEu83AIbX8+0T0eYOxYYelrx7rg5Enz5zs9GVTf//DDYgBWpYqz+YlljPqecNTHhQuDu89sUXDJEv+Te7MBcyB1183WgsQ/Rkp0fR2vVSu4Z8tKdKsdYGZK9HAsTsUzW7YIZYoalJWTWV/0/VOi1im7SvRAlEknTojxBABMmgTMm+e9ZiY3cnKA229Pxvvv1zO8HotK9FB2MpL4JFRL9PHjgZtuMjeE+N//fM8lat8WDJHwiR4OzAKL5uYCzz0nXFzNmOEbz2jePG+8IzOluFon9XVTTm/HEl3NIy3Rw08oY7gCBRTk5oZuiS7XQaNxe58+xvG01MWcxx83fs+jj5rfa8SLL/ru8H/iCaHHe/lle88gweHoVMLj8aBz5874/PPPsXfvXowcORKLraIzkTxycqw7ADUSsVGnbbV6piI33tq1A8xcBDl0SARb0xMPSvSMDLHNbds2oECB4J8jd/L68r/88sCfZ8cn+uHD9p9ntaXOSXbsEBNcQChb9FHqSeiEQ3kVqjsYI376yfecncmdkSLDrD67pZ6T4DAa0Mr1eeRIoEGD4J5t153Ll1+KoEpqf6vWSyqFfZFlX7582t1q/L18YfA9/6SmBn+v2lbvvtv4vJ7164EPP0zCnDnVceiQ7/VYVKLrueii0H5TEvv4q8dHjwqZp9/BqvLII8KHtZGyHDBWdjZpEng+ExU5htGePe5RBNtx53LunPG4Te2T/Llnka8/8ggwf751erM8cpwWGkb9QigLYTk54XHnsnOn97ORLm3GDOD//s/3/CefiMWcp57S1l1AxEp65hlxrz/3tp99BvTr5425I5OUBBQrZn0/CR3XNOlixYphxIgRWLt2rdNZiQk+/NBeB6DvtN94Qxt5Wm34Z8+K6NyqkJA7BDsD9FWrgDFjYDjYDydmiox4iVFbvLj9AFdmWLlzueWWwJ9nx5IokNAGZn59neaRR7yf3TJQjDfCYQEUiVgNe/b4nvPnL1GfRv1sx2qdxB5GC4VyP1GuXPCTJLtK9HvuEfJbDRxKS/Two1qrB0I8yAsG3/NPOJTods/L/sP9WaTFohK9SBGxUMB6ltj4M1aYM0fIvAcfFAp1M86c8f98FdY5+8hz6wsX3CPrZs/2fpbH73Id8nhE8GU9dpXo+rozerT384QJwuLdSplOdy6RI5R6eOECgrJEf/NN7XuPHfN+njvX/F16rMb7/qzbZXr1AqZPN77GuhYd+DPHKHYVrfqO5t57tSv2aoOdPl245OjUSXsesKf8ue464NlnxTMiiVFeli0z90OciJQu7f2clqa9NmBA4M+TBxLrDFykejzazt6fz2orJfonnwjfc2bR0SPJ7797P7tloBhvqIK9Zcvgn2F3i1uo+HNjBASmRHezooP4p2ZN33NyPxHKVme77lxU/vhDbNds314cU4nui1V7s7pWoEBkd9998IGwIHIbtEQXVKrk/dy0qfZasD7RAfM699JLwJo1vudlOedPwePmBVqz792ypTdGAUlc/FmiywonfUB5Of3LL2vnHv/8IxabjRTvlJfBkZ3t/rnRsmXezx4P8NVXvmmCVaLLrFsnlOrLl5unYWDRyOHxALNmGV/zFyNPUTzIzg68MD7/HNi+PbB7/BkY6mW3kW/+YGAfFx0SdJgc+9xxRy5Klz7lN53dwKK7dhmfB+wN0NX7wxEwxAq9oKtVyxtAgQgaNQI+/hj44QdfJXpKCnDNNYE9Ty3/7Gzjyd7p08IVikrnztrr2dnGAxkVeaDy5ZciGvpNN5mvsEYKvQUDCT+qYJ8+HXjhheCe8emn4cuPilEf52+ADRhPAM3SulnRQfzTty/w9ttaJZvedVawisfdu72f7ezqmT0bePJJr5//U/6HAkTCqi0mJ9tTos+bB2zcaHxt9myv2yl9vIWBA4UF0ebNtrIaNegTXdC2LTBtGrB4se9YJhKW6I8+CnTo4HteHoPLMuWff4QMlC1v3bxAS7lHrPC3GGS2I3DzZuCbb7zHf/4p/Ayr3HijMBp79lnfZybqAmGoBLLj2Cmuvdb72ePxnQMD3nrmL1ConX5VH+NLRh/AlPUuvJi5Txw3zv+9WVnBDXBOnAgs/YEDwihQjtlg1efZVaJv22b9Xta16MCfOUbp3l3BDTdYz8L++MO/MlDt5GVBoyhaYRnIAD3SA2a9gsqNFl1O4/EIhY9qpRgqapmarfq+8or2eM8eb+AWQOx8uO4677Fddy79+gH79gWW11CgEj38zJkDXHKJ91gV7FWqiO25wRDoIMYO/vwh5uYKV1UrV3rr7+7doo9VoTuX+CZfPuDOO8XkXEVviR7swFW2lgtmoqq3mCXWWLXFpCTzIHUyXbp4f3e9vLjhBlFP9u0D2rQxvv/ff+3lNVrQEl3g8Ygde5dfDpQqpb0WittAq3H0kSO+52RL9H//9eCnn8S5yy4DevcWrgTsPNtpuDOLWOHPEt3MmKF1a63CFPAuRisKYOUZNlH7tlCJtCV6uH045+QACxb4nj95UhiZyXJeH5gRsNdHWY3Xvv1WuPygJXpkMFvor1kTeO01YOpU83uzsoLrBAIdn996qzAKfOIJ4+v6uacdX/uA8cK7DPu46BDC5kTiNP4aiZ0gZ3/8AdSvr1WiX7gQuCV6MGmDQf/8hg0j+z7i9QUs71YoUMDcByEA1Ksngm5UqCBWYmUC8Ym+a5c2sI1djhwRPkXLl/efdvt2sa1YzgcFUHi45hpRBo0bi2O3/q7+LNFzc8WkbfNm4McfhYKlbl2tFYo/JTqDvMQHDz4IHD8O9OgRPkt0mWB8/nfsGPp7443KlbWLuTJWk9mkJPvjGCsZuGyZdcBtt1n1UYnuy4ABwsL177/FWOauu4J/VqBjY1mJfvXVyfj3XxG8WB1PyUofNyukuXhMrAhWiW4Wfys31/8uVvZtwRFpJXrr1sBvv/nOGYPliSeM3T927iy+S3q691zv3r7p7PSrVkrP48fFHKhOHXHMehccVaoYnzdzr+bxiF0ogFBiGxGsEn3r1sDSq+O8yZONgyNbWaJb1T9/+WBdiw6O/MytW7fGI488gvnz5+O0HZMfYojHE/rIuV8/4VZBVqKfOxc7lugkvBQo4HvuzjvFYEANdHXHHfbK+Z9/xH99Wn19sqpfx48HbrF34gRQsaKY9PqLU/ztt0JAX3cdLdEjhTzQcauLAH+W6Dk5XvcLS5eKPlK/jdNKid6+vTZwLYldSpUSgc6uvjp8lugywShXQ3EzEa+88QbQrp2x+yd58f3aa313xQSqlDSTF1bPcdtYhoFFfSlcGJg0SSycTptm7sNbjckjx6PRY2fMlJXl9fssL6b9+6+oYHPmeM/Jdc7NimpaohMrAnHnYkc23n8/cMst1mnYtwVHpJXo4X6+WfwktR7pfeyrqHUuVCU6IOYL6u5ZzisD4/PPxY4/M/csZkp0s/Z95ZXezydOGPj5sYG8+zhY7LpzCUWu08A0OjgiSrp164bff/8dN954I4oWLYqWLVti1KhRmDdvHk7RuadtwqFEB0QwR7kz+vLL4C3RIz0wdvNkIR7IzDTeJrRnj1eJnj+/vXK48kqhBNen9Xcs07Gj2EI9frz/98l5VS0E/fmd/eAD8f/bb7V1l4Od8CErzt06eQnUJ7pVECL9s2rXFltHZbc2JD5wixI9lICH8coVVwALFwI33+x77fPPgVatgLvvFvE6Hn7Ye23v3vCNeaye4zYluv57UAZaI/8+x46J32/fPvNt1v7GxtnZYgdfhQrieUYKILP+xc0KaSrRiRV6hZLVTlW1z7SqO6++6v+dbh2Hup1AlNxNmgT+/IIFgf37A78vUoRDiQ543c6y3gXGTTcBc+cCNWoYXzczyjKqo5mZYjFc3X2wenUp30QRRM2Tomj7NP14364luj/kXRYkcjjSpEePHo158+bh6NGjWLJkCa699lqsWbMG3bt3R/FQnA4mGOHskOWG+8UXYluVCi3RY5uhQ7XHVuW5fbtxvcrKClyJDghrdH2ZBWKJrvL88/beBwQmhOTvSkv0yBAPluh6hYZR/b/+emHZwoW+xEHvzkXuNxo3Fi5FAmXTpsDvcWu7cisXXQT88gvw5pviWC43/a4kf1jJGKtrbnPnwn4rMMzK1qwt+vt9jxwRi/6HDwNbtgifrnrk/mb+fP95cQNm3ztfvujmg7gTue4OHQpUrSoWkVT0SvRFi7w7P4KF8jI48uWzNzdasULED7LamWPEQw8JN3luIVSf6HqoRA8vgViiq/XWbPdBpFEU4KmnhFvPn37yni9XTiwWqOjdiAYL+7jo4GiT3rx5M9auXYu1a9di3bp1KFKkCLp27epklmKKcFmiezxaJdLcudrrbvaJTvxTpIj22J8gNxokZWd7t8Xnz29/4GCkVPzxR+2xnTINZJIYyHYoWQjLPt852Akf8m+s/10feCC6eTFDrieTJgkfhitWeM/17ev9rO8vZYwWjbggE79YWaJXrAhs26YNABiNfJDA0S+GyBOQzEzre4NVoufkCD+Z+r7GKdysiI0lzCb2tWtbu5nQL/4bjUHiyRLdLbKfOIu+7m7fDqxbZ3w9JweYMcPXlV6gcHwfGOpOrccf1441qlUzTt+smfgvu4nVB2nW07AhcOml7lL+2elX+/QRwZ7twHFaeLHyiW52zkm58/jjYoFw+XLt+ZkzvZ+N9BdZWcIl6LXX2pf17OOigyM/880334wyZcqgbdu2+OGHH9CqVSvMmzcPhw8fxpdffulElmKSpKTIKNH1BKK4jrSlOC3RA+eGG0SH2qqVOL7zTvG/enVvmkaNxP+HHzbufLOzgZIlxWdFAT76yN67z583rj/yarCd+nXihH3hF8hKbiDbwUhwWCnRjQKt2OHpp4PPjxFyv3L33cBffwlLVRXZD55+O55MVpbvtdtuC18+ibuwUqKrfYs86KVLH3eiL0c1KFXfvsI9mBVWMsafJfqQIaKvGTPGfl7DweefAy1bCjdmKjRQCA/yYrweq3GTXoluVB7B+N53GqPv8eCDwt0SiX/+9z+gadMUzJlT1fC6lSu9mTO14/7s7OACb+uhgikwnnsOOHVKzA/lPkiWHyryDpPHHxf/hw8Hdu4EPvzQ/B3qc91UNnb7VXmuYIWbvls8EIglunrOqUUauzoFo530GzYI6/U5c/yPRwN9HwkNR5r0F198gZycHAwYMAC33XYbbr31VjRo0MCJrMQ04Wok/pToVoJk8WKxEquydSvQv7/wKxoJONELnCZNRHksXCiOu3YVx/IA6N57xeRv/HhzS3S1jrRtKwLSduzo/91m7i2CCVw7YYLWAmXyZKBXL2HtKWPXncuhQ+aDOgqg8BGoOxd/wWABEfAtnHz/vQhI9cMP/tNOmxaYEn3kyJCzR1yK3oLZnxJd71qLuAO5v09KEi5d9uwR8sGfv/ncXHN5YdcnerjHS8eOAbfeaq607ddPWEMNGeI952ZFrBsxK/NevYJ7nn7x3w2xiP7v/8T4fs0a+/e88YYw1Dh/XhwbfQ+jAPYkPvnzT2DdOg9mzqyJiROT0KePt7/7+2/t/FFFrTOymwMAaNoUmDo19DxRmRk4BQuK/3K/Z+SSSV7kGDQI2L0beOUVYZVux4VTuMomHD6hw92vcl4ZXvLlEzHT9FhZortpp4OedevE2FNF7Qdld6IzZ7IeuQlHRMm///6L9957D9nZ2Xj00UdRokQJNG/eHA8//DC+++47J7IUk4TLncu5c8Do0ebXrQbzN9wgttfJfPxx5LawyxNPswBOxJdSpbRb68qU0Q5okpKA8uV9/fqqZGd7Fd+qUqFsWf/vNVOiP/ggMH068Oij3iCgdpg2zft5yBARsGXSJG03Ztedy+uvm1+jkAofgQYWrWpssKQhNTX4/JjxyivAVVf5T7dzp/mio16JXrFiePJG3ImVJbraT8r14ZZbgFWrxEIzcQ/6cgSEfLPTX1kp0a0m4S+8oH1GOJk6VchKM/chqqJj927vOSrRw0Owvr7tWKKblVGkyu6WW8T4/vbb7aVXFGGQ8c47gDqVM8obAyEnDm3aiP+nTqXioYeSMWOGMNYZOVLE3jpxwveec+eAxx6LXJ6oRA8eWdYZjcP1u0TLlTO3Mn/ySd/nhkvJWadO8PeqfVa4+1XWu/CSnCystDdtArp08Z63UqJblUG7dr7nRowIJYf2URSx0+Ovv7znZHcuKvfdF538EHs40qQvuugidO/eHRMmTMCqVavw559/om7dupgwYQK6devmRJZiknB1yBs3Bu/X88gR4/ObN9t//7lzIqL6nDn+08oTC73yngSGHJynWDHvZzN3LmpHrk6A7Gyr3LXLeDL49tvCGu6ZZwIrx2HDfBWVp05p09hVossKBD0c7IQPfxNm/WDXzgKGlRK9e3f/94dCTg5w/LjxNSNLdBK/2LFEl+tDSooIOKqPUxEqXPQLDb0leiAE687l11/tPUPmm2/suVI7cMDe82TkPNBSOHiCXeANhxJ9yxahxPr77+DyoEed0K9aZS+9bDF39qzwb20UFH7AgJCzRmIE1SgiN9fbsb7xBvDyyyKIrhE//BB+l30ybrZGjSXS0rz9Xd++wFdfWe+81MvWSy+1ThPKLs78+YO/94cfgH//FTtUwwnnleEnMxOoWdP/GM7OIs2wYb7nmjcPLX8qsmw0QlG0CnT1nJ17iXM4Zon+5ZdfYvjw4WjYsCFq1aqFb7/9Ftdeey1eMwpJTwwJlyW6P4JRCgXiu3zOHLHad+21/gPGqM+tUQMoUSLwfBEvF10EzJ8vLNb8reIaWaKXK+f/HatW+a8/gW5lz87WChX9pNWuOxcrYUqlVPiQlehGdWHOHG1gHju//UUXabe9yai++40Il2W4mdsXvRKd1p3xjd6CWXYzpCrK5fqgpqclprswskS3y9SpwOnTxtfsxg6R68jffwuf5frg3adOiQXCW27xryS1+x3kdJs2if+FCwNLl9q7P5Ex69vlHX+BYMediz8l+i23CAveYF3KhIo8LktLE/FA9AvO6elAhQrRzRdxjmAW5MyMFMIFlZnBI8ultDRg2TLgrbeASZPEHN5qEVEvl+Q5mJGSU+/OJxBCUaIDwsDL7g4cu3BeGTn8jeGMfKLfeqtWyBoFvzXTE1SsCDz0kP38qe7NzFi7Viw6yxi5cyHuwpGpXMmSJVGiRAm0adMGgwcPRrt27XDxxRc7kZWYJlpK9H//FYMa2XLZH4Eo0eUB05kz1v6O1U6FlgThwch9hZEAysryDp7UQdLIkdot6UYULBh8cE8zcnOBJUu8x1ZKdKt3Ww2kOdgJH/6U6NWriy3gquLG32+fnAxcfTXQogVgFIe6UCF7eQkF/e4Hlaws+4s4JPbRD9wbNQJee00sDN51lzgv14FI+WU08gtJ7BOKJboahNSIN9+09wy5X+zUSUymPvhA645Fdnt27FggOTRH/q6ffir+9+ol6nE4AvglIrISvVs3Yc1o57eU5caGDcD69b5p/CnR1d0Nq1cLg5Rwxw7xh6woSE31xuGRado0evkhzhOMMjPSSiMq0YNH7stSU0XMrSZN7N2r/931O/n050IZr4eqRA8kDoRdWO8ih/zbWlmiy9cKF1ZwxRVeOWVkmGk2Vv/9d2E84U8HYpeWLX3Pbdgg/KRzLOZeHGnSa9euxYEDBzBz5kzcc889VKAHSSAd8uDBob2rUyfgt9+EIjU3VwSLsVJQBhsUyd996kSDwihybNzo/awOYows0UuWBB54wPpZZ896J4N33ukNTiMTqEJp4UIRHNXsfruWwFSiRwd5kcNOsB+PxzzgKwCMGSMGyOXLG1+3eke4XK389JPxeb0SncQ3+kmgxyOUquPHe3c9BOMTuHdv72fVp6wVPXr4T0PMMZrMm12PBHK/pFojLVpknsbf4pxd+SV/L9UKq3Nne/cmOvXri/96eSPLu3HjgNKl7T1Plht33GEsR8zkl9H5QYPsvTecyMpPszpo5AObxC+pqUBqamDWBPq+L9xw/hg8csyHQHfd6PsEo3KIZyU655WRQ94hYfQ7q+NxWV+QkqIdS9WoIdxIyYtCRvqJ/v2F4Uo4+xEjS/UnngAuuQTYsSN87yHhxRFRQqV5uLA/MJk82dpiyh8rVgjfUCNGCCXWxRcDY8eapw9EkSR3Yv4mh2fPiv+0RI8c27Z5P6v+wIyU6IDxdjt5C9wHH4jgn4AQOEaDokAHFr/9pj3W1xm7luhWdYiD7PCRlia2Rt5yC3DFFcZp9Ba9VtvR/bX9Ro3Mr4XLMvzHH43P79kTmfcRd2LHDYhRH2Tm/kNFnpxaBUAGgIkT7S1OEXP8lWOkJ79GdURvbS6n8Te+CkaJrj6/Zk179yY6L70k3Bfod0PJbTcpyX5ZqGNbK8zGM0ZuEL/4wv/zLlwwj2sUDLIiINpBUIl7qVYtsPRW8YrCAcf3wdOhg1Aijh8fuJJb/7sbuXOR0wQbXwLwVaJfcklg9//7b/DvNoP1LnKoi9qA9neePl0YBrz4ojjWK9FlPB6h35o40XvOaL6pPj8aiyKKAuzcGfn3kOBwrEnPnDkTPXv2RIsWLdC4cWPNH7GHothvwR6P2GYu+74Ohv/7P+C558Tnp54yTxfIdjy7SvScHOHGAQgucBaxh7x1SLU6+OYbYyV68+bCL5g88W7XTkww9Zgp0QMdWOifoVcomCnR9eloiR49HnlELKiYWa7IZaoP0KjHX32pXNn8WqSDfg4dGtnnE3dhR4luJNP8+XyVrb381fc+fayvE//4K0erPiUcGPVLeuWs3me2+t+ofoWiRKeBgj3q1BGB9PSW+7KMS062P76xE8Bs82bj82PHAvv22XsPIMZCOTlA3bpiB4LZzqpAkcf9VJYTlaefjt72vG7djM/LQSqpzAyefPnETtFRowK/144lupzGTEn/1lv+3yXvVgaAX37xf0+kYb2LHO3bez/LdahPH+C774BmzcSxXAbJyUDRor7P0qcxI1rlaRScW096OlCpUuTzQrQ40qRfe+013HrrrcjMzMTq1avRrFkzFC9eHFu3bkWXULW8CURubuCaviFDQnun3grYjBUrgJUr7aW1685FVjwcPGjv2SQ0VJ+aH37otZ7UD2yef167/dLjMR785OZGRomurzNGPqnfflt8l+nT7b2XSvTocsUVwpLg5puFEsLq9/en5FEDOhoR7Ym9rAwl8Yc/NyCAsUy7+27r5+qtWc147DHrQLrEHv58ovvbDRAqdhb35L4rJ0f4SK9RQ2w9DtaFlJESnRP90GjVCqhdW8TsqFUremOJP/+0l27jRuH7NTMT2LJF1J3Vq8OTB9n6nZboRKVq1egVutEc44kntPWOC4XOYMcSXS4nMyW6nfK77jqxo7VuXeDwYfMAt9HceRWtOHaJiN4Qywy57jRqpGDMGGEkoRqH6tMY1TW1jkZrrGRnx0cgu95I+HBkuPzWW2/hnXfewRtvvIG0tDQ89NBDWLBgAYYNG4bjkQ7LHUcEo0SXFUyvvhrGzBgwbZq9dIFYoqsEEhWZBI8sWNRJtj9FuJnF+f79xucDnVRZ+UAHjC3R77pLbJceNsw4z3oojKLLRReJACpqcDur39/fwMUqmFo0ldrJycDo0dF7H4k+wVqit2yp3W6sd8fSoIH3M5WakcdfOUbatsNMiV63rghSq0+TmyuskrduFQpQ/c48u/JLlqVUooeHzEyhqF62TCyGRev3tFvm06YJV0GRcFcguzOjspyohCugux1atPA917Kltj6yj3MGq8CiRq5bzOqNnfJLSgJmzBCLi1aB10NxGRMorHeRQ64rVr+z3A9cfrmCJk2EC9uHH/aer1NHxNzKzAQaNgReftn4WdHSE8j+3s2gzsIZHGnSO3fuRKtWrQAA+fPnx8n/HPr1798fM2bMcCJLMUn16keRP7+CWrW856680vqeevWAQoXEylukJ4Z2g44YbVP2l87O9hYSHKri/OWXhUWVHn9+zc2U6Dk5xuftCAizd6nPNTvW1yd54mhlzUCB5CyhKNFTUoBLLzW+9sQT5vcVLWp9PRCaNxc7N+T4ACT+CNYSHdD2PydPahd42rQR9bhYMaBCBe952e8jCR/+LNEDoVOnwO85cMB4l+DGjV63LvpxktW4Sf4+VspMOR2V6JEhWr+nv90IL74o5KJR0MZwuTkz2gVISLQUlR99JBRfevQBBNnHOYORO5fBg8V52d2OSqhKdDsEUzeDnR9yXhk57Fqiy/oGM6OqIkVEgPc9e4S7s/vv115X+5KiRbUuVC67LKAskzjAEVFSunRpHPkvmk2lSpWwfPlyAMC2bdugcORlm5Ilz2HnzmysW+c9528wXLKksGzauFFsBY6kRbeVWwUZf5boEycCAwZ43YlwK15kefhh4S5HLzhULrrI95x+wGKmLDeKMh3oVnR9ejvuXIzQ51lWVnGQ7V78tf+kJOH/cN8+EehMDRLTrZsIiHTokLasVXJzxQKjFTVq2MtjejpduSQCwQYWBbR9TEqK1xJm5EixeHnwoAgoJNdJfb/K4VJ4sFOOdgm23U+eDBw96nteneTL9ahDBxHkXcVKiW4lX+U6qKaj7Asv0VKcGJVzVpZYyH32WTHWX7nS2CWjfkFm6FAx5g4UWUHBvomoRMsSPTXVuP+SDSvKlaMy0ymqVtUeJycLuXfwoHC/Amj7DTMFtx0dgF05FoxrWKt4cFZQtkYOuU5Y/c6yjLIy9ExONu+31Dqamgr89ZeYUx48CMye7VwZ5+ayX3MCR4r7yiuvxDfffAMAGDRoEO677z5cddVVuPnmm3Gd2pPaYMmSJbjmmmtQtmxZeDwefPXVV5rrBw4cwMCBA1G2bFkUKFAAnTt3xmZdZJ527drB4/Fo/nr16qVJc/ToUfTv3x8ZGRnIyMhA//79cezYsaC+e7jJyBAdQZUq4lgNnmBF4cLe7eNGQRXChV3lqD+f6PfdJ3xyq8VLQRR5zPzsliplrCTQd95Gg5+cHKBHD9/zRpbocnRsf+n1bo3krVdG9enBB4XvTllAPvywEIAqFEbuxV/7T04W9a90adE3DhsmtnTOnCmulyhhXOdyc/0Pzh9+GPj2W/95pAI9MQjWnQvgW9eeeAJYu9a7E6hoUaBgQW2aaG6LTyTCqUQP5X6jfkn15aqvR0uWeD/b3cGnx8idC40UwouTluhffAFMmQKMGWN9r1y3/vxTBO778MPQ8kCf6EQlWnIrKclcid6rl3AZuH49x/dOUb261o2s6se5RAnj9KFYotslK8v3XOHCwJo1QNmyxveMHi3mkXaQd6aZPY+Ejl1LdFlGhaNfSk8X9bdkSfG3Y4dwtedP5oabs2ej+z4icEQd+c4772DMfzVsyJAhmDZtGurUqYNx48Zh0qRJtp9z+vRpNGzYEG+88YbPNUVR0KNHD2zduhVff/01Vq9ejUqVKqFDhw44rZo0/8fgwYOxb9++vL/Jkydrrvfp0wdr1qzBvHnzMG/ePKxZswb9+/cP4ptHjp9/Fsod2a+THfwJo8qVg84Snn5aBL/64QdgwgRztx12faKr7vI5yYsu7dp5P5cpY5xGr4Qwc+di5K/63Xe1x/XqWS8GPf649vj4cWDWrBpYudKD/fuB/za2ADCuTy+9BPTs6c1znTrAM89ofRRzkO08ZlYCcvs3spbT9w8ej/AtLCu2jepFbq69wbmdhUqzdkLiCzvuXBo3Fv/1C5P6epqUJHyhW8k3/TUqpMKDW5ToRopQtYwDUZRbWaJv2+b9XKQI8MYbwNdf051LpIjW5Nao7pw4Ye9euf5cuBB8HmiJToyQ5wPVqkXuPR6PsfxMSRHX6tc33klLoofsatHIV7mdwKLByqiuXX3PGeklNmwQvrDr1jV//yWX2Hvn9deL5y1cCHTowE4xUtj1iS6XdyBjtQ4dvJ+tZFv58mKxyG79CJY+fbTH1at7dTXRDJab6Dhi15SUlIQkqZb37NkTPXv2BADs2bMH5cqVs/WcLl26oIuJY+/Nmzdj+fLlWL9+PerVqwdABDTNzMzEjBkzcLvkrLZAgQIoXbq04XM2btyIefPmYfny5WjevDkA4N1330XLli2xadMm1JIdkjtIuXLiT7c+4Bd/nUiDBsI3VLB88AFw993ic8WKwI03+qaxUqLLx9xu7AwffeR1fyELEhm77lzsLIB8801gW+xmzUoCUBfffaf4bFW2Ujyo9alTJ5EvOwoxEj1++w1o1Mi3T5DL6ZVXRB9jdt0MMyW6v/rZrJn5FtMiRcRiTFIScMMN/vNAYh87yteePUW9koOFAsEtBtMSPfKEOr4I5X4jReihQ8CkSdYLc2+8Iazj1IUaI1/nKmfOeD9v3Qrce6/2OsdX4cXIhR0AvPaaNtB5qFgtwPhDrSPz5wPffReePOiD3QaaJxI/yHLrkkuALVsi8x4zS3QaXrmHFi3ErtBChYzdKsr9g1m5ZWQE9+4pU3zlqJESvXx58d9qHuivTjVoADzwAHDzzcKAp04dY6t3Eh6C8YkeCB9+6N1JYEeGRbrPefRR4QLpppvEcaVKwkCwZUv/sRFJ+HDNcHn//v249957Ub169bA87/z58wCAdNVvCYDk5GSkpaVh6dKlmrTTp09HiRIlUK9ePYwcOTIv0CkALFu2DBkZGXkKdABo0aIFMjIy8Ouvv4Ylr+EkUOWfvwlTqAFhdu70fjZTxssd0oULwNy5wD//+F6jpZQzlC8P/PGHcDNgtkVJX++MfEvbUaKvXClcEwVTxocPe3wUBnas99Q8ye9kHXOehg2NLc3lsila1Ndi3c7gJVhL9Hz5jPvEsWOBZcuAe+4Ri4alSvnPA4l97Cy8paSIiVSdOub32oXKgMhj5srMLqG4yDNzP3b33V6fsUa88opWGW5lie7PzR5lX3To3Dm8z1u71vfcihX27lUUsVjTqZO1Kz1/yAoK/eIMSVxkBVfx4sLYIBJ4PObuXIg78HiEkYmdANxmY6qOHa3v++QT4/OlSwObNmnnsXrF9o8/+s8X4H8s1rQpcMstdO0YLeTysNKDqQskgSIvvrhBiZ6crDVKTUkBihUTgXojuduHaImqaDl27BiGDh2K+fPnIzU1FaNGjcI999yDsWPH4qWXXkK9evXw/vvvh+VdtWvXRqVKlTB69GhMnjwZBQsWxIQJE7B//37s27cvL13fvn1RpUoVlC5dGuvXr8fo0aOxdu1aLFiwAIBQ7mcahPvOzMzE/v37Td9//vz5PEU+AJz4b19lVlYWssKwHKk+Q/8sMUHy1fKYvVNRkgCI1n7ZZblYulQ7AklKykUoay0ZGTl5z79wIQdZWb6zxOxsbx7efz8HEyYkI39+BUePZv83qUzNux9IRnKygqysIJcTw4xZOcQbtWqJP8B4NV2udzk52bj8cgX6epidrZa9cX0qU0ZBgwbZyMqCptwD4cKFLM192dlqnfN91pEjom4rikgjJoDe75CVRZOpQIhEW8jJSYa+vqSkaMvmwgVt2ebmZvm1+FCUFADakVZurgIgF2pfZERubtZ/AzTtOx95RP3u1u+NNG7vj0LNl1Ny1Qy5LiQlBdZnJCd766D/94l3KIpWHnv7t/jBqTr88cceLFvmwR135Jq0Y3vy6NZbszFlSnBD63PntPIL8B8sXuWHH8S46MQJ4LffvP3m+fPa/lA0H/PvkpMj0ru9L4kdjH/r9HTfsg6FJ5/0PaffpWVGVlYODhwwHid50/ivB1lZvvJaT26ue8bvdnFzW3C7TAUARfHWdY8nB9dem4sxY/zX/SefzMHjj9vXRuXmqnNGbf+rKP7HhPGOm+uwTJEiXn1ATo5xH+nxZGHJEg+++MKDt99OQlaWdizftq15eVepIuLPvPxyCs6d86BGDQV//CHur1dPQZs22dK9xv1ZVlYWFMUDKxVabm4usrK0K9axUgaxiFBsq/N38/Lv3Vv4LM+ffxWyshoYJzJFPN+obH3zY10/QsX7HUWekpP958lNuL0t2M1XVJXojzzyCJYsWYIBAwZg3rx5uO+++zBv3jycO3cO3333Hdq2bRu2d6WmpmLWrFkYNGgQihUrhuTkZHTo0MHH/cvgwYPzPl988cWoUaMGmjZtit9//x2N/3Nm6jFY1lIUxfC8yvjx4zFu3Dif8/Pnz0cBNVJUGFCV/SpZWUkArvFJN3fuXMP7N26sCqA+AMDj2QtAu0x38OAeAAZ7rmyyadNGABcDADZs2IS5czf7pNmwoVpeml9+OQigDM6e9eDrr79HcnIugO4AgH/+2QagOnJysjB3bgh7TiOAvhwSjbNnkwF0AwCsXv078uffB+BaTZqkpH3YvfsCgCqGzyhY8BjmzhXR0rZsyQDQLuB8LFq0GIDX58zKlVvw2Wf/APB1hjdlihgcbd++FXPnbsC+fQXz7l23bi3mzt0d8PtJeNvC7t2NAFQEAPTuvRF79hRCevp6zJ0rO27V1rPvv/8OqanWyszz5zsD0JqI5OQo2LBhHYBGpvctWbIQpUqdxW23VcX779fPO2/WvzqFW/ujM7IviSBwSq6aoSjA9dfXxdGj+ZCd/QfmzrWvHDp//ioAIs/+64+o4//+ewSA11R6y5YtmDt3o+13xhLRrsOFCgFXXQWsWmWW4lqzC3lcd91m/PnndgBXBZWHH39cFPS9Fy6IcdGIEe2wfbt3v/u8eQtQpIh3QvDPP9ay9aeffkCRIt7+1a19SexgXG9+/nk+gKujmxUT/v57MxYt2gOgvWmauXPnQlGAY8fyoWjR84Zp9u5tDsDYNabKqVOnMHfuTyHk1jnc2BZiQaYKJaeYx+3evQM//7wFdvq5qlW/h9HY3Yzff1/1nzKtueb8r78uwc6dp2w/J55xYx2WqVOnAFq1qouLLz6MefN2QK03Mup4qX174PPPO+DAAW0E9h9/1Mo8I0aOLIX58yvhuuv+wejRbQAAHs9hzJ3r9TBw6FBLAL5GlHPnzsWaNaWhr2cyu3btwty5awyvub0MYpEDB/IDEFsUfvllKfbuNQ8IoqoaAy8HdRy+E3PnGmz9kvj990wALX3OP/LICvz8czmcPp2K338PfsvykiWL8PffZ/LydPr0Hsyd+3vQz3MKt7YFu3I1qkr0b7/9FlOnTkWHDh1w9913o3r16qhZsyYmhrJ/0IImTZpgzZo1OH78OC5cuICSJUuiefPmaNq0qek9jRs3RmpqKjZv3ozGjRujdOnSOGDg3O/QoUMoZbFnf/To0bj//vvzjk+cOIEKFSqgY8eOKFKkSGhfDGKVZMGCBbjqqquQKvkXMFs86WoUUQPA1q3eVdbMTN/Q0RUq2PNPr6ddu1wsWpSEypW9e9irV6+Frl1r+KT96y9vHipUKJW3BbVt204oKMnGypWF8jU9PdX0+0Qbs3JINOT+plGjxujaVavEvPzyXLz2WibeftvcSmnw4CJ55bpmTXD5aNNGuxA3c2ZNzJrlW+dkqleviq5dK2PrVu+5Sy5piK5dA12lTmwi0Ra++MJrhfTBB6qrL+tJerduXfxupUtN9RV9iuJBw4b1DVJ7ueqqK1C+vAhQ9MsvCjZtEgup7I/sccJupDsTnJKrVlydpwcLLJps9erJOHRIfLZbf4rrInFVq1YNXbsaL0rGKm6vw1ZUqVIVV1xROej7L7usXdD3pqaKcVGPHtrfrH37qzQualautPb516lTBxQtGtvlEAt07twRDz+cg+efd95HU9WqNdC6tfUe8K5du+Lee5MweXIyPvggG717+y5Uv/22/+9SrVpB18hLu7i5LbhdpgJiN4xKrVqV0L69PcOsnj2vQr9+9t9z6aVN0Lq1glWrcjF/vneuceWVlyNMnmJjFjfXYT233goAmcjONo7sKfcfRYqk+MRf6NTpKr9u1bp2BR5/HABKoGDBHHz8sQfPP18UrVt7n/3WW8b9mXi/rxwtUkTBiRPifIUKFdC1q1anEktlEGvslmzeLr/8MtS3mMoFWw7jxuVg3jwPxo8vhwYNrHVj+fL51o+0NAVjxwrj3KFDk/C7gc57xIgcTJzoX45eeWU7VK4MPPVUDr791oMXXiiNRo1iR666vS3YlatRVaLv3bsXdf8Ld1y1alWkp6drAnxGioz/olBs3rwZK1euxFNPPWWa9s8//0RWVhbK/OcAqWXLljh+/Dh+++03NGvWDACwYsUKHD9+HK1atTJ9Tr58+ZDPwBlWampqWCuM/nlmxvFm75RPC9cu+uvBuXLJl0/cJ7Z3qiQjNdW3c5B92KWkyFvVUzW+7BQl+b/0Htc1unCXa6wh+6VOSUnR1KuSJYHFi5MAJFn6JkxP99aPYP3IpaT4loHYVmVOWpp4r/zO1FTtdyD2CWdbkPsGs2empwPnznmP8+VL9RsbwsinnaJ4kJZmLRLT01Pz6oX8DLe1fbf2R6HmySm5GglefFFMFkeOtP+7JOkcviYlGcvUeMCtddiaZOTLF3x5tGoVyvc1HhclJaVqZJn/uA/a9LFZDu5h4kRgxAjf82lpqXj2WeD556OdI1+SkpKRlGRdb1NTUzF5svj8wgspuOUW3zRW/va7dhXb6MePTwp6XuE0bmwLsSJT27ffge3bK6Jfv2Skp9vrIwN9f1paCkqWBL7/Hpg6FbjtNnFeHrclOm6sw2bIxjDXXCOMq4YN09YLozmlXob549571RgO+vGVcfrU1FRI4fbymDHDk2dUkZRk3s/FUhnECpUqCYOWkyeB+vXtlX+g5fD44+rCi3/51bIl0KQJcPAgsGuXOCfrrsxe+9JLyfj6a2DbNuvnq3X80UfFn4tCXAaEW9uC7TlZhPOhITc3V5Ox5ORkFCxY0OIOa06dOoU1a9ZgzX9mq9u2bcOaNWuw879oll988QUWLVqErVu34uuvv8ZVV12FHj16oON/USm2bNmCJ598EitXrsT27dsxd+5c3HTTTWjUqBFat24NAKhTpw46d+6MwYMHY/ny5Vi+fDkGDx6Mbt26oZbqJNpFBBoUSlY2GSmW/AXwMEMtZlm5ZTZZMAoeCgBnz2qPGVjUvVgF2JONWazKLpCJvhl2/cfKqO8qV04EsyxRAmhuvkuPRBE79eCtt7yfu3WzF1zZqK8rVAho0UKUv4w8kJc/2wkuQ4gZrVsDf/8N3HGH0zkhgaIPZqxiJzixFVJM+4BRFOM+SQoBBICBRaPN8OHG55OS3PNb5+b6rxcyZ88an7d6xl13if6upe8Od5IA3HvvGmzcmI1GjUSQvv+8pfrl8ce1cwgr5LGf/JmBRWMTuQxvuAHYuVMYHcgY9aHRCMBu9A639OeJSFIS8L//AYsXmyuoo0lGBrBypaizKnKdMasrHo9QiuvHmN9845uOOE9Um7yiKBg4cCCuv/56XH/99Th37hyGDBmSd6z+2WXlypVo1KgRGjUSPmzvv/9+NGrUCI+LpSLs27cP/fv3R+3atTFs2DD0798fM2bMyLs/LS0NP/74Izp16oRatWph2LBh6NixI3744QckS7V9+vTpqF+/Pjp27IiOHTuiQYMG+Oijj8L0q4SXQBuW3JD799deGzVKRJgOBrUDkJXoGRnGaeWJnzwIv+cerVJUvRYNAUkCw6jejRolzssBrzp3Nn+GPNANdjDy5puB36O+KzUVWL0aOHAACb/10y0MHizKpWdP8zS33ir6hpwc34GGGXKfM368+P/SSyJ47oEDgBxLWu6TqEQnTqLvZ1kHncHMXWJurrOTm2wDl/w7dmiP/S00UxEQfgYO9D2n1pPS1t7JokKgSvStW4FLLwU2bBDH//d/wgDhJwtX56xXRCUlRSiYVNedVowbBxw9au+5ch275BIxD61YEbDwvEpcjB1ZKrvhVAlXX2P1fiM9hMcjjCOSkoDuvq7cSYIj10srJfptt/mOMfX1jfLUHUS1GAYMGIDMzExkZGQgIyMD/fr1Q9myZfOO1T+7tGvXDoqi+PxNmzYNADBs2DDs2rULFy5cwI4dO/DUU08hTVreqVChAhYvXowjR47g/Pnz+Oeff/Dqq6+iWLFimvcUK1YMH3/8MU6cOIETJ07g448/xkUXXRSOnyTseDxA1ar2J3JyQ2zRQjuoSUkRA3y1SAxiz5ii/syyUtNs8mZkbQ4A8+cbW6mz83AfqalisOrxiMkUIJSTR48Cffp403XpAlMfh3KdDbaMZatku8jCyeNh/XITrVsDhw8Dn35qnS5Qqz65Xxk1Cjh2DLjzTu+zDh70XpfjXWvdS9l/HyHhok0bp3OQmDzzjPj/0ENCZhi56MjJcU5+KApw3iDeo16xTkv06PP++76WZep4xw1GIYriv14MHqw9XrnS2yZefhlYt876flrOERmPR+u28amnzHfiyH3SZZdZP1PlkkvEOO7vv813DhH3U62aKNf/vOn6oLrskYmGDDOzRF+8GDh0CLjWfxxykmCY7WqWMRsX6Os0x2nuIKqbnKZOnRrN1yUsa9cKpdAzzwBvvw1ceaV5Wr3iUl4b8HiEi4N//hGDm+3b7efBaNBipkQ3s0TXD+zV+90w6SBaPB5g/XrgxAnhFkXFaE3MzvpTpm9A9IgRwLodcYAwxbbSoO+LrOqAXF9piU6cZv58USfPn9cu8JDIMno0cOON3l1KRi4CGjSwJ98ixYULvudWrxYGEHv3Ap984l+hxMlZ+PF4RL2QF2fVsbfV752SoiA7O/La59xc/zsU3nvP95z6fT77zP87WK+IHrlOlCgh5ptmXHYZsHQpcPvt4r+/5wEc28cDa9YIfUb58sbXjTwCR7KvUZXjFQxi43o8Yo6gs8MkBIA9dy4yt9wCfPihiAdAS3R3wmKIQwoVEgLnlVdEgJXZs83Tykr05GTjRl6iBFClSmB+5YwGQ3aU6HPmaK/JVlSqQp2dhzspXFirQDfDzOJJrovFigFlyxqnU6lY0X7erOjbNzzPIbGDXQV4aiqQP7/3mEp04jTp6cLP4vLlQKdOTucmcfB4gJo1veMP/TikalVg0CDRX2zZAjzySHTzd+wYcPq07/lnnxUL3P/+K9yp+bM4ppFCZNBbYvtTom/YAKxda+CfJwIcPKgGJwuMQMbitEQneuT6o84ZVdd9d92lTfvNN0J5rnc7KlO4cHjzR5xH1WeYUaCA77lIuXMZPhxQPflWqRK595L4xI47F5lJk4QOb/p0KtHdCoshjklPF4FB7a7GJyX5uraQCWRyZbQ6bEeJricry/uZSvT4wKwe6Ovb//2f7xZimXBN9o2irJP4xq4CvGpV8y14wQSxJSQU1D4yM1MEP6Ziyjn045ArrvD2D1WrAvfeG/08/fqr/zSffGJ9neOryKD/Xf0p0evUAWrUCG8eihY1Pv/hh8DChYE/L5D+h/WK6JF37ah+yydNAmbMAJ57zjet6m/ajAYNwp1D4naMDLci0dfMnCniJlkt1HA8RoxQg2nLIR/t1NECBYQOr3Bh8/EDcRYOaxIcWZnkT4lud5W/bl1jBefx48K38e7d5nnQI29PpjuX+MCuEr1sWRE00oxq1cKTH/pLTDzsKtHV7Zkq9IlOCAH8T2qcCBh59qz/NP68KnJyFhn0rnbU+iP/3kZWleGkefPwPi+QusJ6RfSUKwd88YXW7WixYkCvXvbd+JUo4f0s+1gniUGfPsDEidpYMeHqa+Tn3HCD7258VTmqwoVCYsTHHwMTJohdgSqB1lFaorsTFkOCIyuCzNy5qFx8MfDii76CQ0/+/OauX3r3BipXFsFejPKgR7ZEP3fOOF8ktjDzGWu0Y8KsrO+7D3jyyfDkh4syxAz9wqJcH6lEJ9GmVi2nc0BU3Bjo6aefQn8GlZ2R4cgR7bGRJXqkZUq4xzq//Qbs22cvrRvaB3EfN94ogroHWzcbNfJ+Zh1LPAoUEG5WZs8GHnxQLMpEQoluxLRp2sVyyk5iRNWqQmch+8oPVNZTie5OWAwJTiCW6B4PMHIk8Pjj1s+U/YYaZgUw+AAAJqRJREFUkZMjlAGqRbpdS/QvvhD/6UYhthk61NcP+V13AVdf7ZvWaFBSq5ZY1a1c2fwdgfhL58An8bA7gElKMneHRSU6iRbffgsMHAiMGeN0ToiKG7fXqv5ag8UJFzSJilpf5Ngvdsa2tWoJq8hgCLcS/d9/gXr17KV1Q/sg8cf5807ngLiBEiWAF14QizLRomZN4eJFhYpNYpdQleiBxCgkkYNNnuSht0Q3G/RaCYpq1YSFcPv2/t+nKtGNgmGpyJboKsWL+382cS+VKontTatXA+3aiQC4b71lvBXTaNKn1svUVPN3cMJGrLC7EJeUBAwZIrYaP/NMcM8gJFS6dhVuOPwFWybRw44lunyuV6/I5icc3H230zlIHNQxyvjxwOWXA+++a29iXagQ8N57wb0zErvujh61l44KJhIJqEQnkcLOPNKOzoQQPaEo0W+91RuImTgLhzUJjt4S3c7WUrkx//or8Pzz3uPvvweqV7dnnZKbC6xbJ1zEmGGkRO/Tx/+zifu55BIRzGrECPM09esLBebNN3vPqUFr5ZXYggUvYP787LxjqwnbHXcA99wTVJZJnBCIJXqtWsCPPwKPPBLcMwgh8YcdpaC8MDxjhnm6O+80vzZypP08hQpdm0WfVq2AxYuB22+3J1MUJXgrNCfLlwomEglokUkihZ0+Sx4HcKGQ2CXQ+aOsC5kyJTJ5IoHDJp/gWLlzMbO0bNQIqFJFKJfq19cOYlQhYmUlLD////7POo0+GBPAiV4ikZYmFJiffgrcf79YfR01SlyT61hSkjZAqNXgp21b1qFEJxAleqjPIITEH/q+wag/sLtrbvhw4yDao0eLLeqB0qEDcNttgd9HhZSz2N3dFOz4xclxDxVMJBKo84Lhw53OCUlE5H6NC4XELoHuZJZ1Iaxn7oHDGpKH3p2LWSMvVgzYuhX46y8xeJEnXmrjtjMZy80Fzp61TmOkROdgPDF5+WXg5Emvzzu5jp08maZRolvVkdRU4JZbRADcaPrPI+4hHEp0QkjiYkeJ/uKLYkw1bpz/Z5lNjDweoEWLwPLm8QSnEOficnS4/HLj8/GsROfEn0SC668X84KJE53OCYk3ArVEZx9H7NKli9BFtGzpdE5IKFBFkODIEz+PRzvItqtoMlKi27VE96dEN3LnwokeAbR17IordiIlxVthrQYzaWlA48bA4cPA559HMIPEtYRDiT5kiPgfbJA3Qkjsoh+HGPUpvXoBJ074D8bu8RjLLPVcerr33EUX+c9bUhJQoID/dABQo4b3My3RI0+HDsKNnRF25FLfvnTnQgghbkDuU2l0Q+zSoQNw5AiwdKnXSGLZMmfzRAKHTT7B0Q/a8+UDihYVn2vXtvcMWZkZiCV6374iuKQVhw/7nqOgIoCoB+XKic916x6x7c5FTVegACd2iUo4lOiPPw5s2gR89ll48kQIiR1at9Yem/UpdpTZHo91X5M/v/fz228Db77pPS5RwhsnRCUpScT+sOLii0X/NX689xwNFCKHan0+aJB5WQ8eLP5fc43vtaJFgb//Bu67L/gxsJNjZ47bSbi45BKnc0ASATUWV9u25mloiU6CpXBhUX8WLQJ27w58xyFxHg5riIakJGDDBmDFCm0wRyuMLNHtCJO9e62DbQEi8KhRHgkBgN9/BxYvzkb79jsDcudCEhu72+at6pHHA9SsScUTIYlI69bAb795jwP1cXnffd7PZpboKrISvUABEYtGpXZtX6MFj0drvW7EQw+J/kvu42iJHjn+9z8RA8hqXP3662JC/emnvtdSUsSugVAUNbREJ/FA4cJO54AkAr16iT77m2/M0zCwKAmVfPm8BoEktmCTT3CqVvU9V7o00KyZ/UGvHDwr3IMbo4kplVZEJTMTaNlSQVKSVjlu5AZIRVa2E2IFB8WEEDNki8hAAw03auT9bKZEv/hi8V+2Zs+f3zeItn5MlJRkrETv1cv7WbVel/PNsVXkKFwYaNrUelydL5+werTriidQGFiUxAP+FggJCQcej+izrfQaxYp5P6u7+AkhiQGHNQlO587AtGnA8uXBP6NLF+C994DvvvPvr7N+/cC24p0753uOg3FihDzQ2bLFPB2V6KRHD/G/eXPj62XKiP/cXkcIMSPQGDJt2oj/Xbv6WrDJx599BkyZAtx0kzh+5BHvtWLFhLJVxcgVTFKSNg0ATJ8O3HabNo0+37REjx1k4xW7RFuJLi8Y0BKdhAsq0YlbaNEC+PBDYNYsoEoVp3NDCIkmHDInOElJwIABoT0jNVX4ebTDLbcYb1U14/x533O0liJGyBYBei67TATwAHz9x5LE47XXxIJev37G1+fMEdvvhw2Lbr4IIbGDrBi0o0R//30x4R40CFi50ns+PV37rCZNgGrVvMd16gjXd/v3Cwt2eZH4+HHfMZGRO5fevYGffvIeq/fQEj020Cuhgykrs3s++gjo3z/w5/lDrls0fiHhokIFp3NAiMDjiUzfSQhxP1Sik6iit7jyh5ESnYNxYkaRIsCJE+Kzx+OdxP30E/Dww2KbtLpFniQuFSoATz5pfr1pU/FHCCFmBKpEr17d2+8ULw7cc4/whVmmjP8AZbIrFlnB/u+/xu5c9Jboeot19R7Z3zot0WOHYMbBJUr4nnvoId8guZGAlugkXDz0EHD2LNC+vdM5IYQQkqhwyEwixqRJwF13Aa1aAb/+Ks4lJwc2+Kc7FxIIsjKhTh0RJDd/frFbYsIE5/JFCCEkfgnUJ3qhQiKQpIqsZPQ3xpHTXrjgGyw7KcnY6tgoCFqHDkCfPkDdugy67WasLNHHjAG+/167u8GIAQOAP/8UboWGD/c+Jxrlzl0OJFxUqiR29RBCCCFOQSU6iRhDhoi/7GzvID1QS/QzZ3zPcTBOzJDr1ltvAfffDzz2mHP5IYQQEv+EurgfrP/orCxf1y3q/WXLAnv3es8bWaLnyyf8pRN3ow9uJ+8aePppoG9fsRBiRfHiwBdfiM9btwI//yxcmpUrJ3Y67NgBLFsWvjynpAgjmsKFhVEDIYQQQkg8QCU6iTj6iVsgk805c6yfR4iMvMDSti2wapVzeSGEEBLf3HYb8O232qCdweDPnYsZWVnG7lz0z9QfcxwVG/TuvRHff18bTz2lrRRGfvD9Id8zcaL22owZ3jS5ub73vvKKiCVy8iRw+LD/dwHC17rshogQQgghJB7gMJpEHP025VAnb7REJ2awbhBCCIkWU6aIgJ9t2oT2nFAs0c2U5frzsnykrIwNbr75b+zfn+2jjDZbOLHCThq9xbtKlSrCev3QIeDxx/0/B6ACnRBCCCHxCZXoJOLIE0IrS/SGDc2fMXKk9zMtqIgZrBuEEEJijUB8ostkZQH79hk/i5bo8Yu+/OwsvNgp8+ee83+v/C7VlVBamv9nE0IIIYTEAxxGk6hQqZL436yZ+UBeHpjfcIP2mmx1w8kfMYPWdYQQQmKNYN255OQATz2lPWdHiU5ZGdvYtUR/5hn/aWSGDAE+/9z3vFn9XLwYOHBAuM8jhBBCCEkEqI4kUWH1auDvv4W1uR0lun4S2bRp5PJG4gcqBgghhMQagbpz6dRJ/O/Vy9cKWPVpbaVopTFCbJOii2hlVme6dBH/Cxa0Pz4qW9b3nJkhS758QGYm0Lq1vWcTQgghhMQ6HEaTqFC0KFCjhvgcqO/Gdu2A9u0jki0SZ1CJTgghJNYIVMH9ySfA7NnA5Mm+ci8nx/g5tESPH6wWSAoWFP+7dAEaNQKWLgXWrLG/cGKUzswSXc3HqFHA11/bez4hhBBCSCyT4j8JIeElUEv0K6/UThgUJTL5IrEPFQOEEEJijUAt0YsVA667TnzWj6lUJXqjRsCmTcBFF/mmoyV6bNOggdjhqSrM5fL8/HNgxw6gRw9xHKiVeJEivufM6o465sqXD+jeXRyr9Y8QQgghJB7hMJpEHXkALluYN27s/ayfUMrblQsVilzeSGzTooX4X6qUs/kghBBC7BKoEl1Gv3icnS3+v/gi8PjjwPff+6bjgnNs8+yzwBNPAAsWiOP8+b3XatUC7roLKFMmuGfXrQtce632nFxfjCzRVahAJ4QQQki8Q0t0EnVkJfpjjwFjxwrLmVGjgPfeE+f1k8j0dHFtxw6gVauoZZXEGM88AxQuDPTu7XROCCGEkMAJ1ErczBK9fHlg3DjjdLREj23KlhVjZ5WSJYGJE4GzZ4GqVUN7tscjAozK7ln8uXMhhBBCCEkUqEQnUUc/kbvsMvEnY2SVNWhQ5PNGYpty5YDXX3c6F4QQQkhwhGqJbmYNTJ/o8c3w4eF7lr4Omi3A6Bdj6tQBNm40dglDCCGEEBIP0BaFRB071lDyefpAJ4QQQkgiEKgSXT+OUt25WKWjJTqxwioorZUl+uTJQMuWwKefRi5vhBBCCCFOQkt0EnXsWEMFOokkhBBCCIl1AlVw0xKdhBt9HbTrE71NG+DXXyOXL0IIIYQQp6ESnUSdU6e8n+1YohNCCCGEJAKhunPJzTVOV6ECUK+esFSvXDmorJEEwa47Fy7GEEIIISTRoKqSRJ2bbvJ+NlOW0xKdEEIIIYlGoOOfVq2AAgW8x/37G6dLSwPWrQM2bADy5Qs+fyT+0Y/Nq1UzTkclOiGEEEISDVqik6iTkeH9THcuhBBCCElkjIKp26V8eWD/fqEkP3YMKFXKPC13+RE7yHXwu++A4sW9x3KcIirRCSGEEJJoUIlOok7Nmt7PpUsbp6ESnRBCCCGJgKyYDEbRXbiw+G+lQCfELuXKeT/LY3aASnRCCCGEJDZUopOoc8klwIoVQP78QJkyTueGEEIIIcQd0IiAOE316sCqVaIuVq2qvSaP29PTo5svQgghhBCnoRKdOEKzZtbXZUsX+TMhhBBCSDwRijsXQiJB48bG53v2FBbo1arRtz4hhBBCEg8q0YkroeKcEEIIIYkG/ZYTN5OaCvTq5XQuCCGEEEKcgUN14kqoRCeEEEJIIlCwoPczleiEEEIIIYS4E1qiE1dCJTohhBBCEoGBA4E//xSu7lI4MieEEEIIIcSVcKhOXAmV6IQQQghJBMqWBaZPdzoXhBBCCCGEECu4aZS4ElmJTqssQgghhBBCCCGEEEKIU1CJTlyJogC33AKULg3cfLPTuSGEEEIIIYQQQgghhCQqtPElruWDD4Qy3eNxOieEEEIIIYQQQgghhJBEJaYt0ZcsWYJrrrkGZcuWhcfjwVdffaW5fuDAAQwcOBBly5ZFgQIF0LlzZ2zevFmT5vz587j33ntRokQJFCxYEN27d8fu3bs1aY4ePYr+/fsjIyMDGRkZ6N+/P44dOxbhb5eYdO4s/vftK/5TgU4IIYQQQgghhBBCCHGSmFainz59Gg0bNsQbb7zhc01RFPTo0QNbt27F119/jdWrV6NSpUro0KEDTp8+nZduxIgR+PLLL/Hpp59i6dKlOHXqFLp164acnJy8NH369MGaNWswb948zJs3D2vWrEH//v2j8h0TjTlzgB07gC5dnM4JIYQQQgghhBBCCCGExLg7ly5duqCLibZ18+bNWL58OdavX4969eoBAN566y1kZmZixowZuP3223H8+HFMmTIFH330ETp06AAA+Pjjj1GhQgX88MMP6NSpEzZu3Ih58+Zh+fLlaN68OQDg3XffRcuWLbFp0ybUqlUrOl82QUhNBSpWdDoXhBBCCCGEEEIIIYQQIohpS3Qrzp8/DwBIT0/PO5ecnIy0tDQsXboUALBq1SpkZWWhY8eOeWnKli2Liy++GL/++isAYNmyZcjIyMhToANAixYtkJGRkZeGEEIIIYQQQgghhBBCSHwS05boVtSuXRuVKlXC6NGjMXnyZBQsWBATJkzA/v37sW/fPgDA/v37kZaWhqJFi2ruLVWqFPbv35+XJjMz0+f5mZmZeWmMOH/+fJ4iHwBOnDgBAMjKykJWVlbI3099RjieRYKH5eA8LAN3wHJwHreXQaj5olyNf1gG7oDl4DwsA3fg5nJwu0xVnyX/J9GHZeA8LAN3wHJwHreXgd18xa0SPTU1FbNmzcKgQYNQrFgxJCcno0OHDqbuX2QURYFHimjpMYhuqU+jZ/z48Rg3bpzP+fnz56NAgQI2v4V/FixYELZnkeBhOTgPy8AdsBycx61lcObMmZDup1xNHFgG7oDl4DwsA3fgxnKIFZkKuPP3SzRYBs7DMnAHLAfncWsZ2JWrcatEB4AmTZpgzZo1OH78OC5cuICSJUuiefPmaNq0KQCgdOnSuHDhAo4ePaqxRj948CBatWqVl+bAgQM+zz506BBKlSpl+u7Ro0fj/vvvzzs+ceIEKlSogI4dO6JIkSIhf7esrCwsWLAAV111FVJTU0N+HgkOloPzsAzcAcvBedxeBqqVW7BQrsY/LAN3wHJwHpaBO3BzObhdpgLu/v0SBZaB87AM3AHLwXncXgZ25WpcK9FVMjIyAIhgoytXrsRTTz0FQCjZU1NTsWDBAvTs2RMAsG/fPqxfvx4vvPACAKBly5Y4fvw4fvvtNzRr1gwAsGLFChw/fjxP0W5Evnz5kC9fPp/zqampYa0w4X4eCQ6Wg/OwDNwBy8F53FoGoeaJcjVxYBm4A5aD87AM3IEbyyFWZGqknkkCg2XgPCwDd8BycB63loHdPMW0Ev3UqVP4559/8o63bduGNWvWoFixYqhYsSK++OILlCxZEhUrVsQff/yB4cOHo0ePHnmBRDMyMjBo0CA88MADKF68OIoVK4aRI0eifv366NChAwCgTp066Ny5MwYPHozJkycDAO644w5069YNtWrViv6XJoQQQgghhBBCCCGEEBI1YlqJvnLlSlxxxRV5x+qWtAEDBmDatGnYt28f7r//fhw4cABlypTBLbfcgscee0zzjFdeeQUpKSno2bMnzp49i/bt22PatGlITk7OSzN9+nQMGzYsT/nevXt3vPHGG1H4hoQQQgghhBBCCCGEEEKcJKaV6O3atYOiKKbXhw0bhmHDhlk+Iz09Ha+//jpef/110zTFihXDxx9/HHQ+CSGEEEIIIYQQQgghhMQmSU5ngBBCCCGEEEIIIYQQQghxKzFtiR5LqBbzoUZSV8nKysKZM2dw4sQJVzrlTxRYDs7DMnAHLAfncXsZqPLPagdZIFCuxh8sA3fAcnAeloE7cHM5uF2mAu7+/RIFloHzsAzcAcvBedxeBnblKpXoUeLkyZMAgAoVKjicE0IIIcQ5Tp48iYyMjLA8B6BcJYQQkrhQphJCCCHhw59c9SjhWr4mluTm5mLv3r0oXLgwPB5PyM87ceIEKlSogF27dqFIkSJhyCEJBpaD87AM3AHLwXncXgaKouDkyZMoW7YskpJC9yZHuRp/sAzcAcvBeVgG7sDN5eB2mQq4+/dLFFgGzsMycAcsB+dxexnYlau0RI8SSUlJKF++fNifW6RIEVdWwESD5eA8LAN3wHJwHjeXQTis5VQoV+MXloE7YDk4D8vAHbi1HGJBpgLu/f0SCZaB87AM3AHLwXncXAZ25CoDixJCCCGEEEIIIYQQQgghJlCJTgghhBBCCCGEEEIIIYSYQCV6jJIvXz488cQTyJcvn9NZSWhYDs7DMnAHLAfnYRmEBn8/52EZuAOWg/OwDNwByyE0+Ps5D8vAeVgG7oDl4DzxUgYMLEoIIYQQQgghhBBCCCGEmEBLdEIIIYQQQgghhBBCCCHEBCrRCSGEEEIIIYQQQgghhBATqEQnhBBCCCGEEEIIIYQQQkygEt3FvPXWW6hSpQrS09PRpEkT/Pzzz5bpFy9ejCZNmiA9PR1Vq1bF22+/HaWcxjeBlMOiRYvg8Xh8/v76668o5ji+WLJkCa655hqULVsWHo8HX331ld972BbCS6BlwHYQfsaPH49LL70UhQsXRmZmJnr06IFNmzb5vY9tQQvlqvNQpjoLZao7oFx1HsrV0KFMdQeUq85Cueo8lKnOk0gylUp0l/LZZ59hxIgRGDNmDFavXo02bdqgS5cu2Llzp2H6bdu2oWvXrmjTpg1Wr16NRx55BMOGDcOsWbOinPP4ItByUNm0aRP27duX91ejRo0o5Tj+OH36NBo2bIg33njDVnq2hfATaBmosB2Ej8WLF2Po0KFYvnw5FixYgOzsbHTs2BGnT582vYdtQQvlqvNQpjoPZao7oFx1HsrV0KBMdQeUq85Dueo8lKnOk1AyVSGupFmzZsqQIUM052rXrq2MGjXKMP1DDz2k1K5dW3PuzjvvVFq0aBGxPCYCgZbDwoULFQDK0aNHo5C7xAOA8uWXX1qmYVuILHbKgO0g8hw8eFABoCxevNg0DduCFspV56FMdReUqe6ActUdUK4GBmWqO6BcdReUq85DmeoO4lmm0hLdhVy4cAGrVq1Cx44dNec7duyIX3/91fCeZcuW+aTv1KkTVq5ciaysrIjlNZ4JphxUGjVqhDJlyqB9+/ZYuHBhJLNJdLAtuAe2g8hx/PhxAECxYsVM07AteKFcdR7K1NiE7cBdsC1EDspV+1CmugPK1diEbcE9sB1EjniWqVSiu5DDhw8jJycHpUqV0pwvVaoU9u/fb3jP/v37DdNnZ2fj8OHDEctrPBNMOZQpUwbvvPMOZs2ahdmzZ6NWrVpo3749lixZEo0sE7AtuAG2g8iiKAruv/9+XHbZZbj44otN07EteKFcdR7K1NiE7cAdsC1EFsrVwKBMdQeUq7EJ24LzsB1ElniXqSlOZ4CY4/F4NMeKovic85fe6DwJjEDKoVatWqhVq1beccuWLbFr1y689NJLuPzyyyOaT+KFbcFZ2A4iyz333IN169Zh6dKlftOyLWihXHUeytTYg+3AedgWIgvlanBQproDytXYg23BWdgOIku8y1RaoruQEiVKIDk52WcF+eDBgz4rNSqlS5c2TJ+SkoLixYtHLK/xTDDlYESLFi2wefPmcGePmMC24E7YDsLDvffeizlz5mDhwoUoX768ZVq2BS+Uq85DmRqbsB24F7aF8EC5GjiUqe6AcjU2YVtwJ2wH4SERZCqV6C4kLS0NTZo0wYIFCzTnFyxYgFatWhne07JlS5/08+fPR9OmTZGamhqxvMYzwZSDEatXr0aZMmXCnT1iAtuCO2E7CA1FUXDPPfdg9uzZ+Omnn1ClShW/97AteKFcdR7K1NiE7cC9sC2EBuVq8FCmugPK1diEbcGdsB2ERkLJ1KiGMSW2+fTTT5XU1FRlypQpyoYNG5QRI0YoBQsWVLZv364oiqKMGjVK6d+/f176rVu3KgUKFFDuu+8+ZcOGDcqUKVOU1NRUZebMmU59hbgg0HJ45ZVXlC+//FL5+++/lfXr1yujRo1SACizZs1y6ivEPCdPnlRWr16trF69WgGgTJgwQVm9erWyY8cORVHYFqJBoGXAdhB+7rrrLiUjI0NZtGiRsm/fvry/M2fO5KVhW7CGctV5KFOdhzLVHVCuOg/lamhQproDylXnoVx1HspU50kkmUoluot58803lUqVKilpaWlK48aNlcWLF+ddGzBggNK2bVtN+kWLFimNGjVS0tLSlMqVKyuTJk2Kco7jk0DK4fnnn1eqVaumpKenK0WLFlUuu+wy5dtvv3Ug1/HDwoULFQA+fwMGDFAUhW0hGgRaBmwH4cfo9wegTJ06NS8N24J/KFedhzLVWShT3QHlqvNQroYOZao7oFx1FspV56FMdZ5EkqkeRfnPczshhBBCCCGEEEIIIYQQQjTQJzohhBBCCCGEEEIIIYQQYgKV6IQQQgghhBBCCCGEEEKICVSiE0IIIYQQQgghhBBCCCEmUIlOCCGEEEIIIYQQQgghhJhAJTohhBBCCCGEEEIIIYQQYgKV6IQQQgghhBBCCCGEEEKICVSiE0IIIYQQQgghhBBCCCEmUIlOCCGEEEIIIYQQQgghhJhAJTohhBBCCCGEEEIIIYQQYgKV6ISQoBk7diwuueQSx97/2GOP4Y477nDs/eFg0aJF8Hg8OHbsmN+0f/zxB8qXL4/Tp09HPmOEEEKiDuVq6FCuEkIIAShTwwFlKiFaqEQnhBji8Xgs/wYOHIiRI0fixx9/dCR/Bw4cwKuvvopHHnnEkfc7Qf369dGsWTO88sorTmeFEEJIgFCuug/KVUIIiU0oU90HZSpJBKhEJ4QYsm/fvry/iRMnokiRIppzr776KgoVKoTixYs7kr8pU6agZcuWqFy5siPvd4pbb70VkyZNQk5OjtNZIYQQEgCUq+6EcpUQQmIPylR3QplK4h0q0QkhhpQuXTrvLyMjAx6Px+ecfovcwIED0aNHDzz77LMoVaoULrroIowbNw7Z2dl48MEHUaxYMZQvXx7vv/++5l179uzBzTffjKJFi6J48eK49tprsX37dsv8ffrpp+jevbvm3MyZM1G/fn3kz58fxYsXR4cOHTTbyaZOnYo6deogPT0dtWvXxltvvaW5f/fu3ejVqxeKFSuGggULomnTplixYkXe9UmTJqFatWpIS0tDrVq18NFHH2nu93g8eO+993DdddehQIECqFGjBubMmaNJM3fuXNSsWRP58+fHFVdc4fM9d+zYgWuuuQZFixZFwYIFUa9ePcydOzfveqdOnXDkyBEsXrzY8vchhBDiLihXKVcJIYSEB8pUylRCnIBKdEJIWPnpp5+wd+9eLFmyBBMmTMDYsWPRrVs3FC1aFCtWrMCQIUMwZMgQ7Nq1CwBw5swZXHHFFShUqBCWLFmCpUuXolChQujcuTMuXLhg+I6jR49i/fr1aNq0ad65ffv2oXfv3rjtttuwceNGLFq0CNdffz0URQEAvPvuuxgzZgyeeeYZbNy4Ec8++ywee+wxfPDBBwCAU6dOoW3btti7dy/mzJmDtWvX4qGHHkJubi4A4Msvv8Tw4cPxwAMPYP369bjzzjtx6623YuHChZq8jRs3Dj179sS6devQtWtX9O3bF//++y8AYNeuXbj++uvRtWtXrFmzBrfffjtGjRqluX/o0KE4f/48lixZgj/++APPP/88ChUqlHc9LS0NDRs2xM8//xxKMRFCCIkRKFcpVwkhhIQHylTKVEJCQiGEED9MnTpVycjI8Dn/xBNPKA0bNsw7HjBggFKpUiUlJycn71ytWrWUNm3a5B1nZ2crBQsWVGbMmKEoiqJMmTJFqVWrlpKbm5uX5vz580r+/PmV77//3jA/q1evVgAoO3fuzDu3atUqBYCyfft2w3sqVKigfPLJJ5pzTz31lNKyZUtFURRl8uTJSuHChZUjR44Y3t+qVStl8ODBmnM33XST0rVr17xjAMqjjz6ad3zq1CnF4/Eo3333naIoijJ69GilTp06mu/68MMPKwCUo0ePKoqiKPXr11fGjh1rmAeV6667Thk4cKBlGkIIIe6FcpVylRBCSHigTKVMJSRa0BKdEBJW6tWrh6Qkb9dSqlQp1K9fP+84OTkZxYsXx8GDBwEAq1atwj///IPChQujUKFCKFSoEIoVK4Zz585hy5Ythu84e/YsACA9PT3vXMOGDdG+fXvUr18fN910E959910cPXoUAHDo0CHs2rULgwYNyntHoUKF8PTTT+e9Y82aNWjUqBGKFStm+M6NGzeidevWmnOtW7fGxo0bNecaNGiQ97lgwYIoXLhw3nfduHEjWrRoAY/Hk5emZcuWmvuHDRuGp59+Gq1bt8YTTzyBdevW+eQlf/78OHPmjGE+CSGExBeUq5SrhBBCwgNlKmUqIaGQ4nQGCCHxRWpqqubY4/EYnlO3nuXm5qJJkyaYPn26z7NKlixp+I4SJUoAEFvl1DTJyclYsGABfv31V8yfPx+vv/46xowZgxUrVqBAgQIAxDa55s2ba56VnJwMQAh7f8gDCgBQFMXnnNV3Vf7brmfF7bffjk6dOuHbb7/F/PnzMX78eLz88su4995789L8+++/qFatmt9nEUIIiX0oVylXCSGEhAfKVMpUQkKBluiEEEdp3LgxNm/ejMzMTFSvXl3zl5GRYXhPtWrVUKRIEWzYsEFz3uPxoHXr1hg3bhxWr16NtLQ0fPnllyhVqhTKlSuHrVu3+ryjSpUqAMSq/Jo1a/J8wumpU6cOli5dqjn366+/ok6dOra/a926dbF8+XLNOf0xAFSoUAFDhgzB7Nmz8cADD+Ddd9/VXF+/fj0aNWpk+72EEEISB8pVylVCCCHhgTKVMpUQGSrRCSGO0rdvX5QoUQLXXnstfv75Z2zbtg2LFy/G8OHDsXv3bsN7kpKS0KFDB81AYcWKFXj22WexcuVK7Ny5E7Nnz8ahQ4fyBg5jx47F+PHj8eqrr+Lvv//GH3/8galTp2LChAkAgN69e6N06dLo0aMHfvnlF2zduhWzZs3CsmXLAAAPPvggpk2bhrfffhubN2/GhAkTMHv2bIwcOdL2dx0yZAi2bNmC+++/H5s2bcInn3yCadOmadKMGDEC33//PbZt24bff/8dP/30k2bws337duzZswcdOnSw/V5CCCGJA+XqNE0aylVCCCHBQpk6TZOGMpUkOlSiE0IcpUCBAliyZAkqVqyI66+/HnXq1MFtt92Gs2fPokiRIqb33XHHHfj000/ztp8VKVIES5YsQdeuXVGzZk08+uijePnll9GlSxcAYuvZe++9h2nTpqF+/fpo27Ytpk2blre6n5aWhvnz5yMzMxNdu3ZF/fr18dxzz+VtoevRowdeffVVvPjii6hXrx4mT56MqVOnol27dra/a8WKFTFr1ix88803aNiwId5++208++yzmjQ5OTkYOnQo6tSpg86dO6NWrVp466238q7PmDEDHTt2RKVKlWy/lxBCSOJAuUq5SgghJDxQplKmEiLjUew4PiKEEJehKApatGiBESNGoHfv3k5nJyqcP38eNWrUwIwZM3wCxxBCCCGhQLlKuUoIISQ8UKZSppL4hJbohJCYxOPx4J133kF2drbTWYkaO3bswJgxYzgoIYQQEnYoVwkhhJDwQJlKSHxCS3RCCCGEEEIIIYQQQgghxARaohNCCCGEEEIIIYQQQgghJlCJTgghhBBCCCGEEEIIIYSYQCU6IYQQQgghhBBCCCGEEGICleiEEEIIIYQQQgghhBBCiAlUohNCCCGEEEIIIYQQQgghJlCJTgghhBBCCCGEEEIIIYSYQCU6IYQQQgghhBBCCCGEEGICleiEEEIIIYQQQgghhBBCiAlUohNCCCGEEEIIIYQQQgghJvw/RBDOdDuTuFsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ppg_signal_all_segments(ppg_signal_with_noise_segs,1,False,\"ppg_signal_with_noise_segs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fe9524a7-20aa-4d16-940c-bd4b0743b62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppg_signal_segs=remove_noise_from_ppg(ppg_signal_with_noise_segs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0cd9d589-483f-4eaf-8900-8159d6f4989e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ppg_signal_all_segments(ppg_signal_segs,1,True,\"ppg_signal_segs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf30aa65-20d3-476f-b702-d64ac8413b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient 1 - Segment 0: 122.12 BPM, Segment 1: 95.95 BPM, Segment 2: 117.11 BPM\n"
     ]
    }
   ],
   "source": [
    "plot_ppg_signal_with_peaks_all_segments(ppg_signal_with_noise_segs,1,False,\"ppg_signal_with_noise_segs_peaks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "465e1dc9-eb94-4f2c-a8b2-17aac07ad425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient 1 - Segment 0: 78.90 BPM, Segment 1: 79.31 BPM, Segment 2: 74.81 BPM\n"
     ]
    }
   ],
   "source": [
    "plot_ppg_signal_with_peaks_all_segments(ppg_signal_segs,1,True,\"ppg_signal_segs_peaks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d9118b-554a-48c1-93f7-caadb41bb864",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
